{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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NY86cOSxcuJAWLVqwatUqnn32WSIiIggLC2Pt2rXMnTuXb7/9luDgYK5evXpP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c69128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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dWqgQHjx4cMDHSnDVrl2bs88+m7PPPhtw/g/89ttvPuEZa9ascYuvZ555BnAC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c257f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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wLPBfpdSNux6cj+STQyFyz5w5cxg+fLhdxS6AAlm2vaB8eigjWM6TESyRlZSK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177de550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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u7u54eXnRoUMHh56TEOISI9boMGKb2iJN00hOTiY5OZl58+YB5ul7liDFUhLD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178ae828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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uSeFsjJfRIygDTKDW2v1J7n/496paSPJAa+2e4aoCGC+CMsAW0FrbO3QNAOPGm/kAAKBD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cc4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lOU+5SAhVaE9Ukim5Mvf4yXKecim2WE/mVWhPVJLpUJZtKWnuq8w9fso4i2AIZb7Q6FSM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\n+f+ed2EgKg8b1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178eea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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FOj2xQcep/MWK2VVWbM9U07KY6muB3YnjyPUY8vxGewepfv1pji3eyz9edSfV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\nKKWUUp6kAaWUUsqT/j8aXeSeFE3ldAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169dcac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=10, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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v234WbT9MsG0TeYsmdteaqk5jqk5cgrlEnYqC+a/RcXsNXz+9i4cK3+O2WcYg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\nQMsEvpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d79208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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QWkdrradprZsAzwOtgHnAOaXURKWUTJdlB43LNZbSrXbQr24/ShUrxSc7PrHp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\n2evIZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169b9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [ 0.74081822]\n",
      "Phi(-1.0, 1) = [ 0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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gI/t7SEWDVlUxhpvRClDCQlECZHd3qZ+RNizUrvdhwreflJKoUIJCkYugdoBKxbshUlrX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seogFBSwiK569OUReupe88KT/SjuftGIYoKFemc0MrViahE7bHNE94bqL5I5NZRGpe9JefZiNOH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11699bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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pRYkRA5mvXbtG//79cXd3Jzg4mJUrV/Lll19mbl8IaNoUuneHFi2081Wtqr03b65tb9rU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\n/wMep4k5szfCiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d225c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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uYUrh0qaNzY8DLqrXRNWqVfnkk08ALZrvwoULpdYJDQ1l/Pjx/Pzzz8yaNUtz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e77c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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5CSuTkXouMq3+lqOAKKfjBcovAwE8OmY1olTO2B2PxxGJRBAOhxEIBOD1euFw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27dvPxMQElZWVYZV6CXPOsXMPyaQkVTCwV+LxeNDrDaxYsYKmpia8Xi9Op4OysrKoVXrxoRvu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\nBCJ18VPy9Y9Go3R3d6e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diNME4iRHtieU3+9ndnaWpaUlqqur2bNnT8aLbSQSxW6X3JQE\nIXm0+XaewJllMsKGxWckEsFmW2RpaUmREGwlnO1EIJE8ZJMYLHXrk4v6cDhMf38/breb4uJibDYb\nNTXVNDU1oVarefLJ+Q2PQ3LNGcPhcNDS0kxtbR1TU1ObngwsLS3FdesFWUuF1k4golGBw4cPYzRW\n4/en5hhk62TldrsZHh4iJyeXnp4ehoYGNxxlryUOKhWYzWYWFhbi8olGwuEILpeLhYV5VCoVFRWV\nlJWVIYoxHA4H0Jrx/vv7B6itrcHtduNwONM44STq6WMpcoFYTMrAWFlZoaamBrd7Gbd7mW9+876k\n20Wj0bhXfYC33rqBV1/9HUtLVh5++L+prGyitLQcp9NJIOCnra2N2to61GoVz9gfJhZK/TuerDvK\npS2XpTjZaDRqLBYrEGPXri46OtqV13cpZOOOP1lABM0+LYX9RczPzvDjH3+H3/zmQa677hPcf39Z\nnACp8Pt9DA4Oxa1+e9Hrt9a5jsVizM7OUlRUyFlnHeDqqz+shH8BLC4ucPvt11FX18RHPvIpLr74\n6owyRTkHQavVbrtjbbMtYjKZKCkpzdouVIZarcFozFOco0wmE/n5+XR0dFJTU00gIJELp9OpOPsk\nhp2tOkTpU6YPMh7lx3y45sa0/5dJwuQI2XjaKrsGPcpF+mvxWqRzs6GhMc09ZQ95ybmgoHBTS87p\nJhBy6Jw8nU2XD/JuIRqNbovMZEMcVm+roaCgIEXaKmUahRVy4XA4mJ2dJRQKKY5yMrmQQzFPJWxE\nIE7UDsSJgCw9uvPOO/nud7/L1Vdfzb59+7juuuvo6enhqaee4uqrr1bOGVEUFUmpTCZONfIApwnE\nKY1YTCp+zGbJfaOxsZGdO3dmvFCsrEgypeXl7bkpbRWZXI5isRj9/ekLTL/fh8ViwefzUVlZRU9P\nz7od6c1gK9Zpfr9fcUvp6enFaNz4Q02j0Sb5m4dCQf70pz9htS7S2dlJe3v7phxm5GMfGxtlaUly\nzamrq48/VipZWQ8OxxKjoyNx8pBZKrQW0gRCIBaLsbS0xKFDfyIYDHL33YdpampaN4ApnQ2lKEZx\nu92MjIyg0+loaGjEZpM65GbzDEBKsJQkw4kiCFGlgIcYVusiVquV4uLi+IRmDLfbjcFgoLS0FL1e\nz8rKdNJx5ef3srKSSgRzcrzk5+eTk5OD3x9IKMI18WVpdVJxnq7jbjJJ1qFnnnkmNTW1abX28u1l\nXH755USjX+eFF36Lw2HniiuuZGhomOVlN9PTA7S27sDn86FWqxjxHSdKMgEXiDARGkw7fZTkJWbK\ny8uTyAPAT6fuVCZLarWK8//pInZN7+aBB+7CbJ7ie9/7X9x33/e55pqbueSSDzE7KxFdealVEIS4\n6496XevZRMRiMSYnJ3G73fT29ioOUInjfKt1jvLyKszmKb71rf+X//7v/+S66z7OVVfdqNjQgiR3\nkXMQthvIluomtfUP+NnZ2ZTwOr3ekOI6lBh25vP5cDgchEJB5v3pQxHtUUvan4M0CUv3WZCo24+K\nUX5m/j98uvYLW87FkCEvOefmGuNuadnLMUQxmnSsiaFzXV0b74W90xDFKFrt5uUmGo2KigojlZUb\nE4eNoFJJGRUGgyHFDU7atQoo5EI2o3j99dfR6XQpcqicnOwD6d4pnEoEQr5WnX322Rw5coTXXnuN\nJ554gi9/+csYjUYikQgOh4Pbb7+d888/n9zc3FOO0KWDapPLNafeJs4pDrnLkAhBEJifn2d+fp7C\nwkIaGxuTLDITIYoxnM4AdrufQCA7adLx48fp6el5R0/w48eP0de3G5D3N5xYrVZUKjU1NTUUFRWd\n0Atcf3//phdzJfJwHIDe3r6syANIFzq5+zw3N8cbb7yOwWDgwIFzNqWBliGTB7tdIg/19fXK/83N\nzZGbm0NZ2cYXVrvdzvDwEHl5eXR2dsZtQdMX92vdbhwOBw6H1Dm12+1otVp27NhBYWFBxt/52teu\nTVuk5+cH+OIXf8bMjBmdTkdzc7Mi55iZMdHQ0IBGo0mRwchdNY1GHU+qljzirVYrJSUlFBQU4vV6\nKC4upqKiEoPBkORos17RPz09xeKijaamRiWIbyvv0+joGEtLS7S0tFBXV7ul+wGYmppmYWGBycl+\n7rrrW+j1Bvbv/wCXX349bW0dxGKSdjoQCMQ72Rqliy1nWhgMOfFp0xglJSWK576MpZCN6/90kLC4\nSnYN6hwePucFSnRlvPTSc9x33w8ZHpYyW/R6A2eddZDPfvYLNDXtSJoEwaprVCISMy2k79VMT09j\nsSwQDAZ5//s/kPE1EIQIzz//FA89dDcTEyMA5ObmcfXVN3DbbV8iEgnT3z+AIAj09m490RtkGd0w\n+fmpU7mhoUFlByIbWCwLTE9PU1FRsekphgz5c2B6eprJyUkKCwuoqKhcx9nHwNKSnVgslrTP4AjZ\nuOHV9ye9xzr0PLT/D5TnbH3vweeTzCR0Oi29vb2bnvq43W58Ph91dXWKE9TyspvOzs6U3Y6TASaT\niYqKipRdhkw4kcRhK1haWsLj8bBjxw4ikYgih5IJRigknQ85OTkp5OLdkj5JafNhJZtqLc4//3yF\nEJ0MSGxIRiIRXnvtNV5++WVeeuklXnzxRfx+P0ajkc7OTvbs2cP+/fvp6+ujpaUlxQ3zJEBWB3N6\nAnGSQy6UYrEYKysrmM1mXC4XtbW17N27NyXIS0Y4HMVm8+F0BlJkShtBSoYWMt732wVBEOJdZxuF\nhUW0tOzYVirretBoNAhCFL0+OwKxVfIA0vNyOp0sLdlxOl1KInNid1hybkhvQ7m26z45OYndbqe2\ntg5RjGIymZTfWVxcRK1WU1RkTXsfsp+9x+NR7Hybmho5cuRo1s/F5XLh9XoVLadaraa+vp6CgnxA\nhU6nQU64ljvtarUmLXkAKRxPpVLT0dFBT09P3DNd+p3CwkI6OzuVDwnpOUi/t9amdGHBgsVioby8\nnLKyciorK6moqNj0B/bExCSLizYaGhq2RR4mJiZYWlqiqalxW+TBZJpR5Fgajcju3Wdx7NjrvPji\nU7z88jN84ANXcvPNn6W1dXUxWxDkTrbk9mK3L2GzLTIzY6akpISGhgacTke84MxFq9UkTR9kiLEo\nP53633xh19e54IJLOP/8i3n99T9x113/wcjIUV5++RleeeV3XHjhB7npps8kHQOkWs/KMjNpWgRm\n8zTz8/PU1NQSCoUQxWjGqYVWq+Oii67mwguv4vDhl3nwwbt5881DWCxzCEKEwcFBRe6yHfKwvLzM\nyMgwRmNe2k76ZnpuUqDe9JYkUIlQqVS43W6cTgdtbW20t69OjuSpxVpnn0hEQKfTEg5HFDL509n/\nnfIex1Qx7p35/pYTlE/EkrMoRlkWndzx1v/DLTl/h7AcpbW17aQkD0CSvG49qNUqKipyqazMQ6t9\n9zrOie4+Op2OoqKilOmXKIrKvo7f78diseD3+5Vl5rXE4u3uoq83gZCdjk4mxyKpASdd43Q6Heee\ney7nnnsuX/ziF1lZkazef//73/PHP/6RX/7yl9xzzz1UV1fT3t7OT37yk6RG4KmCk+fVP42MsNvt\nmEwmQPIk3rVr17ofRKGQgM3mJxgU4rc7uQmE1AGREmcrKipPqEwpE7RaTdbP0edb4fDhw8Ri0NHR\noTjrrC3uUz3ll1lctCEIkbhlpRB3xGpgfHw8ydoyXbd2LWKxGBbLAm63m4qKSkKhEGbzbJIDjd/v\nR61Wx106VGg0WnS65E77ysoKi4uLNDU10tHRgU6nT5smm/h9MBhkcdEad6CRllKlnQcj7e07t+3a\nUlJSEtfQJ0tOdDpdvMuqzkgcACYnp3jrrTcxGvN473vPoqysbEvF2tTUNFarlbq6Wpqatq4Hn5yc\n2jYJATCbZ5mZmUGn0+H3+2loaOE///NeZmeneeCBH/Hcc7/m2Wcf59lnH+fDH76Fv//7rwHS+Z2f\nn0d+vlRIOxxO7HY7PT09tLa2EomECQSCLC9LuQfRaJQ3na+kdZQZWF51lIlEBLRaI5/61BcxGnU8\n9tj9/OEPv+GZZx7jmWceY//+g9x882fZvXtffEK0aj0LycWG2TyLxWIlPz+fSCSC0ZibMTAvUQ6l\nUqnYt+889u07j7GxQTQaLYODQwSDISIRL1//+ue58cZPc+aZ7930OeD1euPL7jnKeb5VOByOEyaB\nWlpaYnJyguLiYtrbk4mIRqMhLy8vpRtusVhQqVTk5OQok8K37K+lvMdCLMKA+82sd40SmxzBYJCB\nAWnq09nZid/vw+v1pmR2pMvqSPzX6XTymHAPx2OHeUT9Yz6/85+pqtreNeXtxEYSJrVaRXl5LlVV\n7y5xkJGNPaj8uZGuOSYIgkIsvF4vi4uL8YTzWNIit9FoJC8vD50us41xtohEIhs2EE+yrn1aQqXV\naikuLubcc8/l7LPP5jOf+QxTU1McPXqUw4cP8+yzz57QXKp3EqclTKcApqamKCoq2rROXkY0KnnP\nB4PJX+Fw+mXbqakpysvLM8qiTgRisRhutxuLxQLEiEQi7NrV9Y6RlsnJCaqqqjd8Tf1+P0ePHmF4\neJjm5uYNnYTkFOzl5WWMxlwqKioQxRjHjh2lpKSEHTtaKSoqSgqWSv5Spfxckt2oMJlM8Y52UzwF\nV53iamO324lEwhmdVFwul7Kk3NfXt+6HSiwWY3l5OW4rC7W1tRQVFRGLxXjzzTcxmUycd955WWdO\nHDiQ2Zr2+edHk15buVgxmWbwej1KWJskycmNa3albvHAwCDz8/Ps2NHCnj3v2bJEwGSaYW5ujpqa\nGlpbN7aezITp6Wnm5xeoq6tNsY7d7P0cPXoUnU7P3r3voaKiMuW5Wa3zPPTQ3TzxxMN8/vNf5YMf\n/AgAoVBIcWOR3vMRjEYjPT3dGd/zxEyLQCBAMBhMyrTQ6XSYTCZiMZEzzjhDmaBZLHM89NDdPPnk\nI4RCQQC6u8/k5ps/w7nnXpj2Q3V+foHjx48hijE6Ozupq6tVTBFkMi3vtEgFaHo5lChKYXo+3wpd\nXd3cccc/8MorzwPQ0dHLjTd+mvPPvzgre0RZhqPVaunt7cm4pD04OEh39/oSJtkMID8/n+7u7i1l\nmMiQ/2YLC4vo6tqFSqVOWtxf+yVPMxcWLOj1UhK3XND7fD7GxkZRqdTU19cRiUTiDZEg4XCYWEzq\n6up0OsVyVD5f1r4H0WgUk2kGQYjQ2NhEbu7618ZV+97Uf2ddM3xb+AeiKgGdSs/P97+QlKZ+smFk\nZISdO3emnFcycaisNKLTnTyWnCaTCaPRSGXl5i2+10MsFiMUCinkQv4Kh8NJi9x5eXnK1CJbq9LR\n0VGqq6vTppP7fD6uueYaXn311RP6fN4N2O12ysvLTzYylNXBnCYQpwAikYgy8j+REMUYwaCQRC5C\nIYGJCRNGY94JiYlfi2hUwGazY7fbyM/Pp7q6BqPRyNjYKI2NTe+Ys9LMjImiouKMFrcgjeb7+48T\njYrU1dWRn5+nFPdrfekFQcBut+FyuSkvL6eqqkpJ9T5y5C2mp6e5+OJLUpbdsoEsh1lcXKSxsWHd\nUCan04HP50/b9Xa73QwNDZKTkxvXKacfD4uiyNKSHat1kbw8IzU1NYokRM6csFgWMBrz2L9/f9bP\nYz0CcejQjPJcEycyKpV8ngbjRW1AkWpEIgIrKys4nc54DshujEbjlpYbzeZZzGYz1dVVtLVlzjnY\nCCeChPj9AY4fP87U1BQ7duxg3759G5Iil8tBXl6+UvR+73vf5PXXX+ZDH/oYpaXSudvT07MlvbBc\neB49ehS32019fT16vSRf0+sNCqELhfz89reP8qtf3Y/H4wagqamVG2/8NBdffBV6vQFRjDE8PMyR\nI0eoq6tl//79Wf/Nr5VDRaMiQ0ODLC976Ohop7S0jOVlF7/+9YP86lf3sbwsWdfW1jZw/fWf4rLL\nPkROTvqOZiAQYGCgH1DR29u77jHJOxCJoXiJ3fTlZTfDwyPo9Xra29tRq1ML/rWBeolSw0Q5o9fr\nxWSaRqvVbXoi5nBIEjV5MhEKhTCZZtBqtbS0tCTtBCU2IwRBcsWLRMJEIhEiEUGRZcj7NDqdnoWF\nBSKRCB0dHZSUlCj7Q+nMAdaz7wX4yqHP8Wrw90RVUXQqHZfXXL9lSdU7gaGhQXbt6lKek1qtoqws\nl6qqk4s4yJiYmKC0tPRt+UzPhGg0mrTILX+Jooher0+RRBkMhqRzZGBggB07dqSdiJjNZv7hH/6B\n3/zmN+/Y8zmRkN36ZIn6SYjTBOLPBYIgvKMjLpPJhCBAWVm1QjBCIYlgbNW9KRgMYrFY8HiWqaio\noKKiMqmYyXYicKKw0bKxTB4k//HejJrq5DC7asrLy5WOqyAI9Pf3s7Kyglar3VSxnYiJiQmsVisN\nDfU0NTWve1u3283ysjvldsvLbgYHh8jJMdDb25e2kIxEIiwuSla5paWlVFdXJ02E5MwJh8NBfX09\noVCQjo7OrJ/HegTiT38ypRCHdF7mi4s2JXFcrdYwNjaKXm+IW7WGCQQCRKNRdDptytRCp9OmvVjP\nzc1hMs1QWVnJzp1tW76gz87OMjNjpqqqkra2zd+P1+tlYcGC3W7H5/PR0NAQdzfa3P1Eo1FuueVS\nzGYptbisrIpbbrmdD37w+i25EkWjIoODg3i9Xjo7Oykrk4oQqfsYJhgMKAQvEAiysuLltdf+wHPP\nPYbTaY8fQyWXXXYdO3b0EggEaW5upre3d9PPTYYc7iftBOyktLSUaFSIXytFgkE/zz33ax5//H6s\nVskp6iMfuZVrrvmosoMh7WRI16axsVEEQWDHjlb0el2KJDGxwJ+ZMWXUKweDQWZmpCK9qalp3Qnf\nekF4KpWKUCjI9LQJvV5PR0e7IjVM7eSnTjFVKjUWi4WiokJKSkoIhcJKfkU21tPpsDqt8DMwMIDT\n6aKuTppKGgyy9ezqIne2U5fR+UFuH72OqGrV5MOgzuHBs/9w0k4h5CmUSgVlZblUV+edlMRBxsjI\nCLW1tW+rqiBbSOGLkRRiEQwGUalUytTCZrPR3t5OYWFhyt/RkSNHuPvuu7n33nvfpWfxZ4/TBOLP\nBe80gVhYWCAUCqXILyQnkGhaOVS6AUksFsPjWcZisRKNClRXV1NSUppW0pDNROBEwmKxoNGo02r3\nNyIPskuUdB9aamtTw+xk8hAI+Ons7GRubk5xmdoMJicnsFis1NfX09zcvOHtPR4PS0tL7NixI+Fn\nkszHYNDT29uXIhMLBAJYLBZWVrxUVlZRWVmRZnE0xujoaNxRqJmKigomJ6fYtWtX1s/lyivrM+aA\nPPaYNIFIRxyCwRBWqxWv10NFhbQY7XK5GB0dTbCfTT6nUuU4ASKRCCqVWiEUubk5OJ1SNkRFRWWK\npelmMDc3rzizrNWor4dVKZ8UjKjT6Zmbm6O4uJiurq4tF9hOp4t77/0+L7zwG5aWrACUlpZz/fWf\n5NprbyIvLztbTFGMMTQ0xPLyMh0d7RltE+XlfLnQjkTCLC97+P3vn+SJJx7Eap0FQKcz0Nd3Ntdc\ncxMVFTXo9XplmpGorYdY2g59LCYiCFHm5mZxu91UV1dTXJx5qheNCgwOvskrr/yOG2/8HEaj1KCY\nmhqmuLiMoqIyZmfNRKMiO3a0kJeXj1qtQqvVxt26Ugv76enp+Huc3G0PhUKMjY2j0Wjo7u4iJyc3\nTXGvjlvcrv++BgKSXbRarUm7G5QNZmfNFBcXYzAYGBgYIBIR6Onpydo5KB1iMYm4uVzOeCp7JbGY\nSDAYUuxnJUIZUpZcE0mFPL2Qn7/T6eCO/n/ksPhHoqx+xp3sU4ihoUHOP38vVVV5WRtxvJtYr5t/\nMkG2nw0EAoyOjlJaWqo4y01OTvL000/T1taGXi9NwO66666TapH6zwinCcSfCySv+3cuHdput286\nJj4cjirTCr8/hNlswWxewGDIpaammry89ScLm7EfPRGw2WwIgkBtbbI7TiJ5kD5sV487GpU64LJL\nVE1NTVqpgyAIDAwM4Pf76OzcRWlpaZJNbbaYmppkYcFCXV1d1lp6n28Fi8WqyHA8Hg+Dg4Po9bok\n8iDtanhYWLAQjQrU1NRmtJJLZxsrCAKjoyN0d/dk/Xzk11YUY3GnnNx1F6PlxOhQKEx1dTWlpdLx\nORxORkdHycvLo7u7e1OSJWkfSCIUMzNmJicnMRpzFflcbm5O0uQim30Ki8XC5ORU2lyFTJAC7JZY\nXFwkLy+PmpoaJT25sLCArq5UUpQNRFFyazt+XHIM6+jo4I03XuTnP/8xk5OS9ekdd9xFR0dvSgje\n2q9oVGB62oTb7aauri6+/5Ja0KfbURCECE6nC7/fT3FxMdPTw7zwwpMsLkpEQqPR0tNzFmeeeQ5G\no6Rx1ut1KXakUtd9tcsOUoPD6XRSX19PTU1NRslMpu5+LP/sWJAAACAASURBVBblppsuYmlpkd27\n38s551zCRRddQUFBQcZdi8Tzc2RkhF27upJuEwwG4/kwsS13+E/0fc3MzFBUVITJZCIQCNDT052U\nmbFZyFJKu91Gc3PzhonVsVhMMY6Qp1TBYJBwOIJarSIcjjA7O8t9ef+BhdSci7b8XdtOej7RUKmg\ntDSXublhDhx477t9OFnj6NGjdHd3nzSWp9ngjTfeYN++fcr3y8vLHDki7SMeOnSIiYkJDAYDsViM\nxkbJEKSjo4ODBw8mNdDWw8GDB3n11VcVElJXV8fo6Gja28ZiMb785S9z9913A3Drrbdyxx13nKwS\npO3itI3raWwNsnZ/M9DrNUSjYSyWOex2O/X1NezffwEqlSY+pYgSCEQIhSSiEYkkjyxk56d3Clqt\nRvG+lpGJPKyVX3V392TseiSTh84ta06npqZYWLBQW7u5RVy1WoMoSp08mTzodDp6e3vR6/WIoojT\nKU1PDAYD9fX168rGYrEY4+NjKZkTarUqK+coGaGQVBQJQpTu7i6lKFrbYZcXty0WC2q1htraGvLz\n85WLtDx5MBqNdHd3bXrfQaNRk5+fx8rKSryo6qGzs1M5RjkdeHnZQyAQiOt1dUlSqNzcHOWD2Gpd\nZHJyirKyUsVaU+6mr3WiEcUokYhkVby0tERBQQHl5eVoNGomJ6eYnJwkJyeHqqoqZmZmEux8Y2uK\n+1XXr7Xfh8NhZmZMADQ1NTM6OkphYTW33vqPjI/3MzbWDxgYHR0D4JVXfkdHx25KSysU1yQ5J2N+\nfg6vd4W6uro4eVvtuGfqrIfDYZaWloAYfX29lJSU4vf7yMnJYc+eA2g0UX75y3s5dOgPHDt2iP7+\nV7nggku5+ebP0NLSwdpdl0hEuiYYDNKuhc22iEoFe/e+J6uJXDp4PG7OPPNsnnvu1xw58gpHjrzC\noUO/5aabPsN733u+QmoTl7jl93N5eRlQKSmyKhWEwxEGBgYQRZGenp5tkYdwOMTQ0OAJua9IJML4\n+BiCILBrV9e2yANI0la7XXIW24g8gES4dDodOp0uJQjO41nm6NGjFBYW8K2Wn+B2u5U9CZ1Oq9jO\ner0ecnJy0Gq37+qzHcjEoarKiF6vYXHx3XdW2gxkK9ZTGUVFRRw8eJCDBw+iUqm45JJLuP3224lG\no8zOzjI6Osro6CgLCwtZEwiAO++8k1tvvXXD2/3oRz/iscce49ixY6hUKi666CJaWlq47bbbtvO0\nTmmcJhCnAN7pC6der8+aQMgSjJmZGUKhEA0NDSlp2DqdhrVBoqnOULl4PL4T+TTWhUajSZKFyV0/\nmTwYjXl4PB4slgUiEYGammqamprW9b0WBIHBwUTysDUP8+lpKTSstrZ2UxdC6XmpiUZFvF4vQ0ND\naLVa+vp6Uas1LCzMY7fbKS4upqOjfUNZhNxxtNnsNDY2Jum+pSTq7Bb7g8Egx48fi3v09yQRAhmi\nKCVa22yLGI15NDc3pxRPkrPNCLm5Oeu6CSUefzoZjM1mY3x8PB7GVY7DsZRiLanVasnLMypyHIfD\noRS2chfV51vB4XDEwxybWFpaine4U4mVIAi43S5WVnwUFRVSVFSM1+vF6/WysrLC3JyUyVFU1ITN\nZsvYRZdC8/Tx6YQqqfseiQhMTEi7RJ2dneTl5SURAmnnYNXZa3JylMcfvxe1WsMHPnAFH/3obbS2\ndsRJ4wThcJh9+86ivn7jYnFlxcfCwgLRqEB7eztFRZKkb2VlhfHxMYqLi+nr60Ov13H22edhMk3w\nwAN38eyzj/P880/x/PNP8Z73HOCjH72NvXsPrJEDSjkHExPjmEwmioqK8Hq99Pf3YzAkT42kYnP9\nQik/v4hrr/0Ee/ZcwNDQGzz77GO89darvPXWq7S2dvBv/3Y3VVW1iowvFovhcDixWi0YjXm0tu6I\n2wrHiESkhkEoFKKrqwuDwUAkEmEzSdyJ58jg4BChUJienu5tSo1Epqen0On09Pb2blsaOjs7i8Wy\nQE1NDQ0NW7c3BkmeNTIyQl5evuJ2FYlEqKmRnLgEIaJMK9xud9LUIl1g3nYcrjaCSgUlJTlUV+dh\nMEjXm1OxGJfzek4ViKK4bt3jcrmUBoJGo6G5uZnm5mYuueSSt+2Y7rnnHr7whS8on4Nf+MIXuOuu\nu04TiNM4jURkM4EQRRGLxYLZbMZoNNLc3LypDympE6wnP1+S1BgMIVwuFW1tVWmdoUKh1QCxEwGN\nRks0KnU3pQL3OKIo0t3djd8fYGpqGoPBQF3d+h16GZLWeoCVlZWM5GEjn3WQunzz8/PU1tZsmjwA\n8cC2FQYHB9FoNHR0tLOwYGF52b3pjI3JyckE56fkomGj5yEX0cFgkGPHjhEKBenq6kav1xEIBJXu\neTgcxmaz4XAsUVhYRFlZKWq1BofDmbS46vF4mJgYR6vV0dq6g5GR0RRP+o1kNSBNZRYW5snNlSwF\n5U58+tcyuYA3Go2KE5fH48FsDtLQ0EhNTQ2iGCUcjiAIQaXQyc01KvkNsZh0bpWWlqHVapX7lWRL\no5xxxhn09PRiMOi31DAIhcL09/dTWlpCT09PVudsSUkpl156Lc8992vl68CB9/G+911FQUFZnDRm\nJg/SjpMXi2UBlUod3wVa7RT4fH4GBqTzsKenB71+VT7R3NzGV77yr9x66+d5+OGf8PjjD/Hmm4d4\n881DtLd3c/PNn+XgwUvRaDRotRqFfHV3d7Nz505AIp3hcEhZ3k7MtFi7RJ+TkxPftZAkSG73Mvv2\nnc0VV3yQ2277Io8//iCPPPITgsEA5eVVyv1brVbl3Gxv70h6DoIQZWxsjGg0Sk9PjyLxSk7izpxp\nIX0vFXXRaJTh4SGCwQBdXduXGo2OjuHxeDnrrH2UlW0vjM1isTA7a6aiooLm5q3bEoPkBDUwMIhK\npaKrq0tpYkSjorIgrtPp0en0Ka+BKEYJhULKlMrlcsXDB2Np5G+58T2WrTffSkpyqKlZJQ4y5PTv\n03j7sF6IHEgOY5n2sTaLf/zHf+TLX/4yHR0d/M//+T85ePBg2tsNDg6ye/eqDHn37t0MDg6ekGM4\nVXF6B+IUgLS8HH5HH++VV17hwIEDKf8nhZeZWVxcpKqqioaGhhNivep2u5mfn8/orx6LxZKkUMFg\ndFvOUIFAgNlZM42NTRw/fpxwOExlZSV+v5+SkmJqamqyXlyUyEOiS03qhW1gYIBduzrXLd5nZkzM\nzs5RU1NNa+vWrES9Xi9PPvkkdXW1lJSUolKpqKmRltdX5RipYU5rC/CpqWlsNuk9rqmpTSuhGR0d\nobW1LeX+olFp6T8UCjM9PYUgRGlqaiQ3d3WBLxIJ43K54xp5KRU1XadWpVIRCASYm5tFp9PT2toa\nL7JTrXQz5WjIXx6Ph8nJCQoKCujs3IVOp0sJzEv8NxOWlhzrLnDLwVhWq4VIJILBkKO8Nnq9XpFC\nCYLA1NQUubm5cYnZ1vTJkUiE/v4BgsEgPT09FBZmtyAtIzFLQs5x6Ol5D9///s8z7sS4XG5FBldb\nW4vRmDwtkuSAAwD09m4sxfF4lvnVr+7jkUd+itvtBKCurpEbb/w0Z5xxgLm5+ax3TGTt/dol+lAo\nzPz8PH6/j7a2NmXvRXIMUhEOh7BY5qivb8FutzM2NsIdd3yBK6/8Kz7ykU9SVrbqCCRbyHo8Xjo6\nOigvT1+kZ0riTvzcjcVEhoeHk+4r26lFuucu7ykA7N27d0sL2DLsdjvj42OUlJTQ2dmZdFyOkI1/\nGfw8/9z9X1m5JUUiEQYG+gmHIynL3GNjo7S07NiSRl96vyNKdon8FYkIa6YWufF/Deu+vvLEIScn\n/bXa5/MxPT1NT0/2+1/vJmKxGIcPH07aJzjZsbKygtlspqurK+3/f/KTn+SrX/0qvb2923qc1157\nLU5k9Tz00EP8zd/8DUePHqW1tTXlthqNhsHBQUXyOj4+Tnt7+4bTklMUp3cgTmNrSPfHsLy8jMlk\nwu/309jYyP79+09oF0ar1a479ZDs3XTk5uooKUkOHJP3KrJxhpKh0Wjw+wMcPnwYu91OTU0NhYWF\ncXlC9n8WieShoyM9eZAfLxqNKjsKazXyMzMzzM7OUlFRSVFRMTabLcVfPtVSMvnnPp+P/v5+lpaW\nFCmT1GUf3dS+gtVqxel0UFZWRiQiYDabkyQ1iZKZaFRArdag1apQq3WAKu57H2Vycori4hI6Otop\nLCxCrVYRDAax2eyo1WrOOOMMxY41XbaGnJo9MDBAT08vvb1Sh34rcDpdLC0tUVNTS09Pz5ayIkAa\nnY+NjZGXl0dXV1cSeUjc39BoJBvPxI681AiQCh2Hw0F/fz/RaJTm5mYmJiZSFrj1+o113/LOjZwQ\nvlnyAFBdXcfnP//PXHjhh3j44R/z6qt/oK2tU3nsaDQal0BocDiWsFqtFBQU0NraSk5OanEaDIaU\nnYDe3uyWgAsLi/j4xz/HDTd8iqeeepQHH7yb+Xkz3/72/yA/v5ALL7yGT3/6/8rqgzpRe5/4eoyP\nj1NQkM/OnW3KbobDsUQoFIrnHEg7QseOHaOkpBiLZRKPx8UDD/yIRx75KZdeeg033vhp6utbGB0d\nVfInMpEHSNzxSU3ilvdWRkbGWF720NbWRklJSdyFKnMS93pI3FMIBAJbJiIALpeT8fExCguL6Ojo\nSLmve0zf4/jyYe4xfW9Dt6RoNMrQkJQW3t3dlSLPkicQW0Hi1KKwMDl0TBSjCqEIBPy4XE6CwSCx\nmCTVTZxaVFcX0dhYkpE4JD6XU8n5RxTFU0q+BBtPIJxO54YTiIMHD/Liiy+m/b9zzjmHl19+mfe+\nd3UR/uMf/zgPPvggTz31FH/7t3+b8jv5+fl4PB7le4/Hk1aO+5eEU+ev4C8Y79YJKooii4uLmM1m\n9Hq9IlN6O45Hp9NtaYlapVKRk6NNe9FPdIaSpVCBgIAgiCwtLXHs2FF0Oh1nn3021dXVxGIxfD7/\nus40cshTNCoiCBHGx8dZWVmhoaGRxUUrCwsLSdIb2UvebJ7FYrGkTdq226VgveLiEozGPEZGRjI+\n38QOuVxkx2IxbDY7w8NDGI1G2tra2L27L8WZRrKhTO8/Lxfts7OzhMMhent72LGjNW3adeJ71tvb\nl5LfIBe1JSUldHV1UVRUiNvtxmq1otFo6eralbJUmQ4rKz5lCbynp2fL5MHtdjMyMoLRmBvfndga\neUjdwZDuR5o4OLBarRn3N0A6Vw0GfTx00E5dXS29vX3k5BjiHXNZirOM1WpNSoFOTOCWO+aCEGVw\ncIhAIEhnZyfFxamJrdlibm4et9vDRz/613zpS19PIvPPPfcEd931Hd73vqu49NJr6ejozDgtkReK\nBUGgt7eXvLzN2UYaDDlce+3NXHXVDTzxxCM8+OBdzM+beOyxe3nmmUe5+uob+MhHPklFRXbp5zKm\npqZZXLTR0NBIc3NyHokgCFitVpaWpH2W4uJigsEgvb37+eIX7+D3v3+co0df5YknHubJJx+hr28f\nBw5czAc+cBkVFVvPKVCpJJmg271Me3s7tbU1QPok7mzkUHNzc0l7CiMjI1suHD2eZWVPYdeuXSl7\nBo6QjaetjxIjxm+tj/Lx5s9lnELIExafb4Vdu7pSinz5NtshO5mgVmswGvPSWnFHImGCwSB6vYjR\nGMbnm+P48Wk0Gk1KyFlubq7yWp5qEiZBEE4pwgMbEwiXy7WhNO+FF17Y9OPKJhjp0N3dzbFjxzjr\nrLMAOHbs2IaJ9H/uOLXOqr9grHdin2iEw2FCoRCHDh2ioqKCvr6+bbmBZIOtOD9tBL1eg16vobBQ\n1tlGWVhYYGRkjPHxUZzOafbvvwCXa5HFRQvZRm2oVBCLSR/Yfr+fpqZGcnJy4iNzNRqNFp0uWT4T\nDAapqKgkPz8/SWpjtVrxeJY588w9tLW1JRT86V1uEhEOS/kIVquVWExk//4DnHHGGYyPj9He3rHp\n12tmxoTT6aSpqWlDCZWcpGmxWOLBP7kKCRwcHCQQCNLR0UE4HGZgYID8/PyMhXU6SPr5AUU/n67T\nnQ3c7mWGhobJzc2JW75u7ZK3vOxhaGiYnJyc+ARDSzQqYrfbsNmkxfSdO9s3JDlS4vEAarU66Xlp\ntVoKCgpSiJUUihaMJ7qudswFIcr8/DyRSCQ+gtchCNEtkSOr1YrJZKK8vDwpSE8O7vv1r3/O4uIC\nDz30A5599hdcf/0nueaam8jPTz5WSaIyQDgcpru7e1uhkMvLHiorm/jKV/6DQMDNgw/exZtvHuKh\nh/6bX/ziXi6++GpuuunTNDdvLPUzm2dZWJAK60TyEIlEsFqtuFxuKisr6e3tTemCd3Xt4sorr2V8\nfJiHH/4Jzz//G44de51gMMTu3fvwer0puxbZdtKnpqax2+00NTUq5AFWpxZri/ZMcih5H016Dyuo\nr6+PT41W9zES07vXTjZFMarcjyiKrKysMDw8jFarpa6ujoWFBRJzOUQxxr2O7xKNu70JosB/vPV1\nrs+7Ne3E1Gyeoaamhs7OXZSUZM7seCcbZSqVioqKQmpqasjNTS5UZTLv8/nwer0sLi4SCASIxWLk\n5OSgUqkQRcmRy2g0nvTWqKcqgch0zJI5RnTbr7vb7ea1117jggsuQKvV8vOf/5w//vGP/Nd//Vfa\n23/sYx/j3//937n88stRqVR85zvfSTup+EvC6R2IUwThcPhtJxAej4eZmRm8Xi+iKLJnz553NHjm\n0KFDafcutotgMMjs7CyLi4sUFxezsLCgdIEvuOCCeNGvAVSEwzEEAcJhkUgkRjgsEo2SJK2BGAMD\ng3i9HnbubKeysnLDY5ienqasrDSp+yYlF89QWVnBzp3Zh5j5/T7m5xcIBAKUlJQwPz+PSgW9vX0Y\njcYtZU6YzTOYzbNUV1fT2tqaUfsu/SsVEX6/n5UVr9I5D4dDmEwzRKNRGhsb0Gq1lJSUUltbuyl9\nv98foL+/H5VKlZV+PhM8Hi+DgwPo9YZt7Rh4vV4GBgbQ6fT09fWhUsHi4iJOp4vy8nIqKyuy+oCW\nlvX7EUWRvr6+lL2BbCGFuw1it9tpaGjAaMxTSMZmU7htNjtjY2OUlpYqqdfhsFRYu91SYV1aWspL\nLz3Lfff9gPFxKc04Ly+fD33oFv7qrz5BaWk5ghCN2xf76eratS3XH4n0DcUnRquWySMj/dx//494\n8cXfxrvzcN55F3LzzZ+lp2dP2vuan19geno6KWU8FApjsVjwej1UV1dTVla+7s6LjOnpaUZGhujv\nf43LLrua7u498ff0MOPjw+zdex7RqIgrssRPvP/O39V+jZqCOoVYqNWrFr8mk4m5uTlljyyx6E62\n6E01B1h7W6fTwfT0NAUFBdTW1hEOS65hwWAwvuAvTTIkyMvbMklJft6hUBiTyYRaraK5uQWdbvW8\nlhOwV3Dzv/x/j8Bqw0eHnm+U/5BibVnS1HJ+fh6n08lZZ51FXV3mhfyhoUG6ut6Zbm5hoZ6amnyM\nxs1dD6Q9vGB8j8aPwWDA5/MprkxGo5G8vDxlaiG95+++dEieaHZ0bL6p9G7BZDKRm5tLVVVq0Gss\nFuO8887j2LFj23oMu93O5ZdfzsjICBqNhs7OTr7xjW9w0UUXAfDSSy9x2WWXsbKyojzul770paQc\niH/913/9c5UwnQ6S+3NCJBJRPjRPJCT5i42ZmRm0Wkm3XVpaSn9/Py0tLVlJTU4UTjSB8Hg8mEwm\nfD4fjY2NFBcX89prryEIAmeddRZDQ0NZPZ4oxpJC8l5//Qh2u4uWljYqKjYmDwBms5n8/HwlF2Ju\nbi6eXFxOe3tHVouhUmLxquONTqdnYECynk1MzD5+/Bi9vX1ZX9hkIlNVVUVbW1vK78nTBqmjKf1s\nbfCbIEQ5evQIMzNmKisrqKmpQavVEgyGFClOYo5Cbm4uBoMh5bHk5dvV57S1Ijux6N/O7sTKio+B\ngX60Wi3t7R04nQ68Xi9VVdWUl2dXeILsknQcQRDo6eklP39rFp3y8rrD4aStrY3q6uQP2EwLxJIN\nZvJ74PP5MJlMFBdLUrNwOIzFsoDP54sH95UlPb9YLMbrr7/Ez372A44efQ2Av//7r3HNNTevMRHY\nWvYJrL5ver2Bvr7etF3GuTkTDz54N08//ahiLrF79z5uvvmz7N9/UDmnrNZFJiYmKC8vY+fOdoLB\nIAsL83i9K1RWVlJcXKQU9OsV8KIosrCwwNzcHOXl5dTV1SalZd9557/Q33+YwsJiLrjgCmz7Fnhd\nfIG9XMBF4eviNqTS1EijUePz+fF4likrK1eIdjbynbWuYGq1Bq/Xy9zcHPn5+TQ0NOB0OhVDiOLi\nooS9JRXyflLiPkXi/UWjIiMjw4DUjMjLMybJJWX8++g/85TlESKxBAKRJjl6enoai2WBhoaGDa1f\n3wkCUViop7o6n7y87XWu5+fnAZIIkSAI+P1+/H4/Pp8Pv99PMCgZEuTk5KRIot7JqYXD4cDtdqdd\nDD5ZMT4+TllZWdocpWAwyOWXX84bb7zxLhzZXwxOL1GfRmZEIhHm5uZYWFigtLQ0nn2wOm14OyRF\n2SAbq9ONfj+REDU3N1NSUkIwGOSVV15RyIPcIc3m8SQLTx0Gg5rx8WPk5Pi58kqpoyY7Q8kEI5Mz\nlFarUQjgZsiD+P+z9+bRjd71+eij3Vpsy1qsxYu8L+NlPPuSpUkgARK2pgkhCxQKJW1pOb97298N\n8Ptx2vLruT2U9sL9cXsKoSmhMMMEGhJCElKWEGDIeBaPPfZ43yRZi7VY+/5u949X72vJkmxJ9gzJ\n1M85OuOxpVf79/0838/zeR6agt/PDq4qlUq0tbVDoVDw2QpbyQOwmdEgEOwsZ3E4HHwXZCt52CqV\nYAuPQqlBOBzB+fPnEYmEcezYcXR2dhRcZ1OKk0AsFoPP5+cdfziNv0AgxMrKMj9bUS152KvZCU5G\nRZIkamvrsLa2BpPJBIvFUtFnNJMhMDU1BYIgshar1ZEHNhF8ARsbAXR2dhSQB6D0ADGQn8LtdDox\nOzsLiUQCpVKJiYkJAIBGo8lKzRQF5EggEODEiTtx4sSduH79Kl544Qzuv/8hzM7OIhqNYmNjDaGQ\ntiiBKO7+lb/LHo2y0hmRSIimpmZ4vb6ichuaZvC+930Ed9xxP37+85fwq1+9imvXLuPatcswmVpw\nzz3vR3v7ATidLiiViqx16HUQBIGGBg1UKiWsVmvZr3sgEIDX60F9fT1qamQIBkM5skIBjhy5A4GA\nH06nFT/+5RlgGIAEmMCb+Gj3n6FBouN35X0+L5aXV9Dc3Ayj0QiSJPIyDuRyBZRKBf+vWCzh72cr\nQqEQZmZm0d3dDbVajUwmgyNHDpc9p8ZZHTMMzX9GRSIRBgY2HZJYggUIBBRPOqYj43nkAQAIhsB0\n5Cr//73MjdgtamvZjsNuiQMHkiQhk+VLKsViMerq6lBXl289y3UtOGLhdruRSCR4WRFHKLjOBSeR\n2kuQJPmWl1ltxXYzEBsbG7u2Jt7H3mC/A/E2AUmSecFn1SIWi8FmsyEcDqOpqQlNTU1F5RdLS6zd\nZbEW4o3C6Ogojh07VtWAGkmScDgccDqd0Gg0sFgsPCHiyEMmk8GJEyd48nDp0iUcOnSorMWVoihc\nuXIFfr8fBw8ezAtV24pizlArK2tZWRSB1VVr1pKyNHng9NncYmk0GvnHmZtbMTS0mZjNYWZmGt3d\nPTs+r1JEhisqcgeji3UlWOtdV3bIXoKRkZGKvblpmkE6zQ4OT0xcQzKZQEtLC2QyGSQSaXaAcdOd\naCeNfzyewNTUFIRCIYaHh6q2GE4kkrh4cRSBQBBdXV3Zblzljhu5FqsDAwOor6/O358Ld/N6vWhr\naysr3C33trma9GAwgNnZWTAM+DkFLmk6kYhnC54kX+RIpRJIpTJIpSwxYZ3EaFAUhdXVVYRCIeh0\nGvzTPz2FZDKOAwcO4Z57PoC2tm6eKOyE3ATttrY2SCT5pG9z57zQrSudTuG3v/0ZXn/9R7wFrEpV\nj+PH78Ztt90LiUQGo9GIurpaiESinNsKih4vd1fe5/NjZWUZWq02a2Na/P3nujN/P/lZ+Fs87NYc\nCRwT346vvPPbAAC/34+5uXk0NDRkB5M3j7U104KzJC2VaZFOpzE+Po5oNIKWllZYLK1ZK+TKi09W\nfjaFRCKZdfKqyz6mzSHuYnVCqUwLt9uN1dUV6PV6dHV1l9VdnZ2d2fMOxF4TBw4rKyuoq6vbdQ4B\nQRB814K75HYtcuVQCoWi6jkGVuIqgNls3tXjvZmYnJxET09P0fV7cnIS//zP/4yzZ8/+Dh7Zfxns\ndyD2wYJhGPh8PtjtdgCAxWLBgQMHtl3YfxcdCO4+KyEQiUQCNpsNgUAAZrMZx48fzyucS5EHgN01\nKmd3hqZpnjwMDw9vSx6A4s5QUmkCy8vL2NgIYWSkBd3dB5DJ0LwzFEUx/PNZX3cjFovBYDDwKdIc\n0mk2MZum6ayXeuGgKmcVC5R+Xk6ngx+c5chDOcSBomhsbGzA42EdhzKZDOrq6tDT01PVCZUr1hwO\nJ9Tqetx++21QqVRZEpZBKsXKcLxeH5LJJLiU6GIa/1QqxQ8oDw0NVkUe2MHwdfz2t+chkUhw1113\nVS3J2RwoT6K/vx8KhRzpdKaoLe/mMCtTVO9us9ng9XphNBoQj8f5IL1iSdv5x0NeAZ9IJLCwsIBU\nKoWmJjNouhE1NTVYX/fkPXaRiHXmIkkK0WgMBBEESZIgiAwoioZYLEYoFEI6nUJrqwUqlRKnTt2D\n8+d/ipmZcczMjKOnZxDvf/9jGBk5WTRvg8vuIAgCc3NzaG/v4Ds0Wwv6nYrQEydO4M///L/jxRfP\n4ezZp+H3r+P111/E5cu/xEMPfRSHDx+EWl3Z++j3+7G6upq1Ii5NHgD2e9I50odIPLRplCQGrgku\nYyPtgyAuxpUrl2EyNfGzJrnIzSvInTPeKkkLBoPYvO7U4gAAIABJREFU2NjAzMw0hELWYECr1YKm\nGSSTKX7WolxQFI3Z2RnE44kCJ69Khriz1+BzIzQaLdrbO8DuOe5EIPbWgUmlksJkUvIhpXuNvXJh\nkkgkqK9nM3ByQdM037VIJBIIBoNIJBK8fexWYrFT14IgiJs6y7gX2K4DUY6F6z5uDvYJxNsE1e0s\nkbxMSa1Wo6+vr2xnFIlEgnQ6XfF97gblWrlyu99WqxWZTAYWiwW9vb0FA2u55CFXtsSBIxDbYSt5\naGlpqfyJAXC5XHzi8OHDh/MeK8Mw8Hj8WFhYQSpFoqvLDIWiDuk0BYLYLP4ymTSmpq6DoqhtXW6E\nQmFWo136sayuWqHVatHTw6b6UtSm7KoYcWCtLj0IBALQaBrQ09OLpaUlJBIJdHd3o7GxOjtLzvaT\nIDIYGNhMUGZJmAw1NbKC940giLyCyu12IRaLw2pdhVgswfDwMNLpNAQCIS9fKuU+sxmkx0rFnE4X\nPB5PNuehH4kE68SytUjftNcsbvdLkiSfm9Lc3IyZmZmqXh+AHdgOBgPQ6fSQSmWIRiMFBTbbGdgs\nyrdq5QUC1m1pdnYGDQ0NOHXqFOrq6kq4fRXeduvnYW5uHg7HGhobDVCr1Ugmk3j44U/gPe95GL/+\n9U/w+us/xsLCdfzjP34enZ19+N//+7uory904OEStGUy6a5mQ1jpoh+AAh//+P8FqZTGiy+ewfT0\nOL71ra/h7Nlv4r3vfRgf/vAnYTJtvwEAcHkfi1CpVAXdglJ4duX/A8Pkf+8Y0PjG3D/hjsh78fWv\n/y/odI14/PEnceed95VVhOZK0kQiEXw+H1ZXV9Hc3Ixjx45BIBAWOHQxDAOpVMbPunASwa072Ow8\nzWaexU5EeadMi0AggJWVFajVan5gd7tMC/b/QtA0syfDxiqVBEajCrW1N4Y4cOCGpm8UhEIhTw62\ngutaxONxBINBOJ1OpFIpsDlJ8jxJFPeevx1dmLYjaXuZQr2P3eHt9anaR1mIx+Ow2+38rvyxY8cq\n1kBKJBJEo9Eb9AiLY6cwOZqmsb6+DrvdDrlcjvb29pJuL1vJQzH7wJ0IBEcefD4fhoaGqiYPNpsN\ni4uLUKvVeeSBez42mw1KpRIjI/0FGlpWt04iHI7jt7+dgERCZYeCSxdaIpGwpGzE7XZjZWUFDQ1q\ndHd387kWQHHikEqlsb7uRjQaRWOjAQMDAxAIBJifn0MwGERXVycMhu0HyUu5ymQyGUxPs/Ke7u4e\nZDJpeL2pkkV57m1zbSVTqTSWlhZBEASMxnosLy9hejqVHV5lP0+cDEcmYwOn2JA2ISiKQjgcRiQS\ngVwuRzgcAsMAjY16eDze7OsiyDrQFKZVc/+KRJKsfSf7+lmtq5DJZDhw4AA0Gk3RIh0QFA3P2yQB\nQjgcawCAkZERdHZ2bPs6l3rt/X4/rFYr3G43BgcHcfjwkarnQgDWftTv96Orq7sgU4GmGRw8eBiP\nPfYkXnnl+3j55XOgaRo22xqkUi8fkKdSqSAWizE7O4dMJlP1bAgX3re0tAy73Y6mpiYcPXoUMpkU\n7373B3Ht2mWcOfMNXLjwBp5//jt48cWzuOeeB/DYY59Cd3d/0WOGwxE+72NgID8scDtMh68WnQ2Y\n8F/CwdRpEEQGc3NT+MIX/hxNTa348Ic/ifvv/wPIZNt3yuLxBF8ohsMhNDc38UPOAAqyNrZ273w+\nP1KpFF9IcqTC6XQhGo2gt7dvV3kWADv8vrCwgLq62jznrHK6FmzgHXLWoZ0D83Jxs4gDh99lkNx2\nXQuWSBZ2LTKZDP83jmAUM7B4uyAQCOzPQLxFsD8D8TYBTdPbFtcMw2BjYwM2mw00TaO1tRWNjY1V\nLxLhcBhra2sYHBys9iFXjMXFRdTX1xfYomYymexQnht6vR4Wi2VbeUo55AEAFhYW0NDQUPTkSdM0\nxsbG4PV6MTQ0hNbW6gYB7XY7pqamIJfLodfrMTQ0BIIg8p5Pa2vrts8nnU7jwoULSKfT/PPJdYbi\n5izSaRLpNIWVlXzLWK74Xl9fx8LCAmpra9Hd3Z2nceZ+5opyzv+cIAhoNNqsrIgGSVKwWq0IhYIw\nmczQajUl5TPb6d8pik3fzmTSaGlpLUimzQVXvOcW1lwRTlE0VldXQFE0uru7oVAoCopxgUAAgiCQ\nyWT4SzrNFlQCgQAqVS3q6urgcDiydrhDWT15brFfHliL1RmEw2H09HTvqjBjZ1RsMBgai7pjbQeS\npODzeeHz+aFQyOH1+iAWi3c1FwIAVqsNDocDZrMZHR3tO14/k0ljY8MHo7EJ6XQGCwsz+B//40m8\n850fgNHYBZKk0NXVhcZGfUUp3AzDIBAIZlO/hdjY2OCdm4o9v+XleZw9+zR+/vMf87NkJ07ciccf\nfxKHDp3g7ysWi2Fqaoq37K3W+hdg52gmJychEgkxPHwQDEPh1Vefx7lzz8DlYuWkarUGf/AHH8Wj\nj34SNTX5pgGxWBxOpxMMw8BgaITVakUymcLg4GBViePAZsbB/Pw8HA4HNBotGhrU2Y4fl8pcWabF\nplOZpKLXLJVKZ7uHMTQ1NfPSxa0olcStVEpgNCr5nJ+bhWvXrqGvr69gkPqtisnJSTQ2NoKmab57\nwXZoBQXuUOz6+bsNyWMYBleuXMGxY8eK/v1LX/oShoaG8Mgjj9zkR/ZfCvs2rrcSShEIkiR5i8G6\nujpYLJY9sV5NJBKYn5/HoUOHdn2scrG6yu7acsNeuQPfzc3NMJvNO+785JKHY8eOFbWB47CysgK5\nXA6TyZT3+1zyMDg4CIvFUuII28Nut2NycjIb0NWN+fl51NTUIBAIwGg0wmAwgAsl4oZSc/9lGAbJ\nZBLj4+NIJtkBx9ra2oLr5f5MkuzAtkRSA6lUiVSKlUH5fEE4HA7I5Qq0tLTk7ZZvgkEsFkcwGIBI\nJEZDgzrrxrM5wOpyuRCJRNDUZIZe31i0WN9up55ziJqfZ3X4fX29aGjQFB1g3bxN8bWMc47JZNIY\nGCivqEokknC5XEilUjAajVAqFYjFYrh27RoikSiampr4ULzcGQu5vKaMWRkGc3NzCAQC6O7u3rEz\nsx1cLrZTxA7bl58RwqUqcxkVdXV1mJmZwW6zJ4B8QtPd3V3VMb75zf8H3/72PwMAamoUeO97P4TH\nHvtjyOXKPOvZYinc7HsgRSgUxPr6etYWWYv5+TlQVHnPb33diXPnnsHLL38fqVQSANDffxBPPPEk\njhy5PRteKOQTwqsFl/nBMAyGh4fyskwoisKvfvWfOHv2aczNTcFobMK5c7+AWMx+vmKxGBwO1iq0\nubkJNTVyTE9PIx6P7zpjA2CD9ex2ex4JZDckNoe3uX8ZhoZEIi3yPrCPNZlMYnKSzWwZHi7vNctk\nCLhcTsRiMZhM5uwAf/5AOftv8SFuhUIMo1GJ+voavjsI4KZlLoyNjeHgwYNvG1nQxMRENmwyv0OT\n27XgrGcTiQRomoZUKs0jFUqlElKp9KZ0LVjjiSkcPlw82+Wpp57Cgw8+iHe+8503/LH8F8Y+gbiV\nwDAM73kOsAu33W6Hz+fLWuW1FCwQuwFBEBgfH+dj228GHA4HSJKESqWC1WoFwzBoa2uDTqcra+FK\npVIYHR1FOp3myQMr0SkszllZhQ0Mw8BoNPK/4xYvr9eLrq4umEymbQv2Yr+jaRoejwcrKyuora2F\nyWSCx+NBPB6HxWIp2y2FIAjMz8+DIAj09fWhvr4eItFmSnWxn0UiEZxOJ1QqFd+B8nq9mJ2dhUql\nRn//EBhGxIfkEQR7IgmFQvD7/VCp2MerVCryBlhZC9FF+Hw+tLVZdhwkLwWSpPhiqLe3t+oB5Urd\njSKRKJ+oazKZUVdXC4FAAJIkswFoSfT396GhoQEMw4AgyLwchWQymdU+i/NcobjdcgCYn5+H37+B\nzs6OAlJaCbj8Aq1Wg97ewoHbYsgNR2tsNECv12dfo91nTwDVE5qtoCga//EfZ/DKK89hZYXNHJBK\nZXjggYfx6KOfhNncknddrphNJhMIh8NIJJIQiURQKhWQSKSwWq0QiYQ4dOhwRQ5X4XAQzz//HTz/\n/L8jHA4CAPR6E+6667342Mf+tEAiUgkyGQKTk5MgSWLb151hGIyPjyKRiOP229+JaDSK2dlpfP/7\nz+Dxxz+FQ4eO5XW0ent7odPtTrrBvY+5wXrbgT3vEDyhyP0uUBQFu30NIpEIIyMj0Go120pjWOLg\nQiwWLUocdoJMJoLJpEJdnZTfYOEuWx8zu+nBSQ/3llhcvnwZR48efdtIgK5cuVIwd7cd2PWPyCMV\niUQC6XSaz5LJtZ6Vy+V72rVIJFjDkaGhoaJ//+QnP4nPfe5zGBkZ2bP73EcB9gnErQRW15pGMBjM\nSj8yaG1thcFguCE7LwzD4MKFCzckGboYKIrC3NwcvF4vL1OqpJOSTqfx05/+FNevX0dvby8UCgV/\nkimFQCAAgiB4q1qaprGysoJgMAiLxcJLqXIL9WIF+9bf+f1+LCwsQCQSQa1WQy6Xo6mpCQ6Hg1/I\ntyMAQqEQJEniypUrSCaTOHbsWEWaT6vVCrFYDKPRCJfLhYmJCWg0Ghw/fjxvoc9kMnA4HHA43Kiv\n10GrbQRNC5FKUUgmiTz9MmcharG0Vj0LQpIUZmZmEI1Gd1UMkSSJqakpJJOpbXdkGYZBMBiC2+2G\nTCaFyWTO04tXQ2Y4CUjubnkmk4HT6UA8HkdHR2c2R6EGMllljjgAm446P79Q1OqzGJLJJNzudSQS\ncRiNJmi1GggEgrzuzODg4K66kh6PNxvsVD6hKQaGYTs0XAie3+/Cd7/7dZw//3MAwHvf+yF89rN/\nn3cbiqLh83nh9fqg0TTAaDRCJBIhHo/j6tVxRCIRtLSwYWyVpnADQDKZwI9+dA5nzjyNYNAHANBq\nG/HIIx/HBz7wKJTKyl633M9muVKjSCQKp9MBkUiMn//8BZw9+w0AwJEjp3HHHe+GwWBBT0/Prjpa\nwGbquFar2daSdjtwMwyZDJutEYlE0dHRAZFIiGQyxWe7yGQ1PNEWi8UIBoN8x4H7jJaLmhoxTCYV\n1OrS0jturc9d84vJJzlSsZuuxaVLl27qxtpucfny5ZJyoEpBUVTerEVu10ImkxXIoarpWkQiEbhc\nLvT19RX9+4MPPohvfetbVW9i7aMs7BOIWwkMw+D8+fOoqanhd7FvNPY6GboYUqkU1tbW4PGwQU1C\noRADA5X5gXMzAsEgK9toaGgoKNCLFeyBQACxWIyXY1y/fh1erxcDAwPo7Ozkr1sJ7HY7fvGLX4Ag\nCJw+fRodHR08mbl48SJOnTq14zEIgsCFCxcQj8dx7Nixsh0nuBOo3+/H/Pw8EokErFYrDAYDTp8+\njfr6ekgkEiQSCdjtdoRCITQ3N8NkMhXdQSIICqkUhfHxKayursFsboXR2AySrDwRnbOL5BxfqnXR\nYDsG00gkEnzHYCu4wWGPZx21tbUwGk0F0gqKojEzM41IJLqrxwOwszvr6+swGIzQaDRIpZJIJJIF\nQXk7acvZ920B9fV16O/ffng3FovD7XaBIEiYzaa8rlY+wTqQZ81ZKTYfUz0OHDiwK/LAdbA6Ojpg\nNm92aFZWFnDmzDfwh3/4abS2soPi4+OXst9nM7RaLQwGA58BwuYWXC/4DFSSws0loVMUhcnJKcTj\nMQQCTvzwh/+O5eV5AIBKVYsPfvBxPPzwx6DV7jzLkktIS302c18Pljg4IZGI0dTUDIVCDo/HhR/8\n4Fn86EfnkEzGAQAWSxc++tE/xV13vQdCoZC3AC5lTlDMISwUCmJpaQkKhTJPtsTdLv+YxYP+uOFn\nmqZht9vR1mbB4OBgAYHnsl1isRhvvywWiyEUCotmWpSad6mpEcNoVKGhofqZHY5E5JKLYl0LYJNM\n7NS1+K9MIEqBU0hsTePOZDJ5rlK5DlGlXuOdkrPvvvtuvhbaxw3DPoG41ZBMJm+azhO4sQQiEonA\narUiHo+jtbUVJpOJn3ko1bosBo48pFIpHD9+fNuZh60IBALweDzo7e3F+Pg41tfX0d/fj46Oyt1u\nUqkULl26hEuXLqGjowP3339/wQJXzutJEARGR0cRi8Vw9OjRsoZwuZMjd7IUCATweDy4fPkyxGIx\nent7kU6nEQ6HkUyyum+1Wg2dTgelUgmVSlVS/jY9PQ2bzYaOjg5+R4hzhmIHtyn+Z4IoHnTIyTBC\noRB6e3uqHizO7WD09fUVdAxIkoLX64Xf74dG0wCDwVB0bmGvHg8ALC+vwO12o7m5ucCRiLuvdDq1\npajd1JZzRW06nYLVakVdXT0GBgaKBuYxDINoNAaXywlAgKYmc0FnoZIididsbAQwNzeH2tpaDAwM\nlO1GVAxLS0tYX/fs2MEiSRJutxtPPfUJ2O3LGB4+io985E9w8uRdEAgEW4ho+V2s3BRuroOUSCSw\nsrICmqbQ338ABkMjZLIaTE5exve+901MTFwEAEgkUrzrXR/EQw99DE1NrXyxnWsaQFEkb4na0dGO\nurr6kkV+JMIaFIjFYmi1WkilkrzCnWForKws47e//Rnm5q4gHmfd8A4dug2PPPJkxa89u2FgQ01N\nDdra2iAWi3PkiYXOX7kuYYW/Y124YrEYTp06WfS7QxAEXC43IpEwTCYTtFotL4PcjuBxRFutVqKt\nTQezueGGnu+2di1KEYvcroVAIMDly5f3CUQF4LoWuZIoNtNns2uRm22xsbEBgiBKGpfcfvvtmJiY\neNtIyN6m2CcQtxoIgijpanMjsJtk6GJgvdq9sNlsEIvFsFgs0Gg229mVDm6n02mMjo5WJfMBWOvB\nlZUVUBQFt9tdFXngiJDdbkcwGERnZydOnDhR9DXbiUAQBIGLFy8iGo3iyJEjBW5UW1GMOHAzD1ev\nXkVtbS2OHTuGUCgEu90OiUSSldfIEY/H8y6ZTCarLWcJhVKpxNraGpxOJzo6OtDfX9zyMhe5zlDp\nNEsqEgkC165dRyAQ3NVg8XYdg0yGTe0OhUJobNTzw92lHuNeDTpbrVY4HM6yHYlykast93q9fABe\nW5sFMpmsYJc2Ho+XlGJxyH+NdqeXD4VCmJmZhUKhyNpyVr8GrK6uwul0obm5CW1tbUWvw72H4XAI\nanUDXn31HF544QxisQgAoKOjBw8//HE0Nlr4Ir2hQcNbgxZzACvuCMZel6IorKysIBqNwGAwQiqV\nZDX+KaTTGZAkCZ/PhYmJ32BpaRoAA4FAgMHBo/i933sAzc256wQDh8OBaJTV9m/dkee+l4lEHKFQ\nCFKpDHq9Lhv6Jioo5D0eDzweDxobG2E2G/Hmm6/jlVeewyc+8X9iePgYhEIBPB43FAo5Ghr0fHFf\nLE07kYhjZmYWNTU1GB4eqtjOOxcMw2B+fiFr4dsFo9GQ93eCIOB2s+9hLnEoB+z7R6C2FpBISMTj\ncSSTSTAMw6cyc0WmUqnc1fPYCdx6WoxgBINB2O12jIyM7Ikc6kaDoiiMj4/j6NGjv+uHUgBOlp0r\nheJIhlAoRH19Pf+er6+vo6enB1KpFHfcccc+gbjx2CcQtxpuNoEYGxvDwMDArluFXKCd0+mERqNB\na2tx285KBrd3Sx4ANi/jpZdeQl1dXUXkgUv2ttlsEIlEkMvl2WyFBhw/frykO8d2BIIkSVy8eBGR\nSASHDx/m5zKK3TeAosQBYDX0Y2NjUChYtyWPxwO1Wo3W1tYd00hJkuQJxeTkJBYXF6HT6dDZ2cmf\nwLmLXC7fcQGnaZrv7HR390OvN/OWs+yFQjlLSqmOQb7+3wiNRrutxIYtgNhB566uThiNxh3vuxQ4\nJxuj0YCurq6qjxMORzA9PY2amhoMDQ3yaezcDnkoFEQ0GuVD8RQKZY4MRwGZTJp18tp9V4UrxMPh\nMKanpyGVStHf35/NFdmUu5SSyWzdlWcYGi6XGy6XC1qtBmZzU8F1Mpk0fD4f4vEEGhoa8taFVCqJ\nS5d+id/85jVEoyEAgEqlxoc+9Cn09OxsL53rHpa7yw6AL/gtFktWky/MK8RZi2AKBEHAbl/FT3/6\nQ1y58mveAranZxAPPPBhHDlyGh6PB9FoBO3tHWhqMhe4koXDITidLigUCpjNpm3XU27IWa/Xo6en\nm/+O0TSd9z3//Of/FKOjb+Bd7/p9PProJ3npVy622sjuJvsD2OwitbW1obm5if99LnEwGo3QanUV\nSd2kUnY4WqMpdNBiGNYdauuGB0mSkEgkBcRip1TmahEIBLC8vAy5XI62tjbIZLLf2RB3JchkMpid\nncXBgwd/Z4+hUqysrEClUkEulyORSCAajeKpp56C3W4HRVFIJBJ4/PHH0dfXh97e3mw3ej8XYo+x\nTyBuNXDOFzcLk5OTaG9vr3oAk9Pab2xswGw2o7m5edudo3IHtzOZDC5cuLAr8sB5TZ8/fx7vf//7\nS+otc0FRFFwuF9bW1lBfX4+2tjZEo1FcvXoVarV6W/IAsATi1KlTBSc4kiRx6dIlhEIhHD58uGhh\ny52ococEcwsKgNWrX7hwAYlEAiaTCc3NzWhubq7YnWthYQFLS0tobW3F4OAgv2jHYjH+BM6GPwn4\nEzfXueC0rQzDYGJiAm63GwcOHCi688zuQFEFpCKVInOIUmHHIBaLweVyg6JImEymslytcndP29vb\n0dRkrug1yQVnZ1quk00pRKNRXL9+HRKJBAcODEAsFvNWvNzgcG1tLfR6HUQicZZYsI5EiQT7bzqd\nAU1T8Pn8SCaTsFhaYTQa84K8ShX3+f9nssVaEjabHSKRiJe7VAquEA8EgvB6PVCrG9Da2ponhyFJ\nAn7/BjKZDBobG6FW1xe1/OUK+eef/y5+/vMXkU4n8e1v/wQKhQpCIUucuFDA3BTtYqGIwNZd9MpI\npN/vwfe//y28+OJZJBLsfILZbMHQ0CkcP34nmpqa8rIUCCKDjY0NKBRKmM3mHS1Oyx1yJkkSf/3X\nn8Gvf/1TMAzbGbnjjnvx2GN/jMFB1voylUpjcnKyqI1sNeDyP3K7SHtBHIxGFTSa6op+zimI27WO\nxWIF+Qa5BKOaTnooFMLy8jIkEgm/kVIMud0KAHk/5+Jmdy3i8TisVmvFc4W/S8zPz8NoNBad83Q6\nnfjUpz6F//k//yfm5uYwPz+fNWbYwN13342vfOUrZd2HSqXK+38ymcSf/dmf4Wtf+1rBdZ999ll8\n4hOfyPsOvfzyy7jrrrsqe2JvL+wTiFsNN5tAzM7OwmAwVDRXwDAMQqEQrFYrMpkM72ZU7kK5k8xn\nr8jDxMQEHA4HUqkUHn744W2vn06nYbfb4fF48ixz19fXcfXqVdTX1+PEiRM7FlvFJGEUReHixYsI\nhUI4dOhQgf3n1hY6d+LZesJ1OBx47bXXQJIk7rvvPlgslqpOmIuLi1hcXERzczOGhoa2PbHnBhPF\n43HEYjFe27q2toZIJILh4WH09/dXfALPZCjE4xlcuTIBl8sDi6UTgAROpxtisRhms6ngJFAKDMNg\naWkJHo+Xt6BlX9fCQnrrjvrWgVUuCZ3r6mzuxFN5xXopCQ13f8kkO+AuELCyJbFYwtvpRiIR1NbW\nQq1Wl/GasfKZQCAAjUaL2tpaEESGT1iXyWQ5RS3niiMpyOsQCIRIp9NYWlqCSCRCf39/zrC3ANvJ\nZPJD/tjPC2dFq9Np0dvbmyNTZHM40uk0zGYT1Gr1jsXjysoqXC5XlvSRaG9nTQ+SyQQ+/vH34u67\n78fDD38MGs3Og/Cbu+jVWxFHoxG8+OJZnDv3r7wFrNncikce+SPcffcDiEZj2NjYyJo1CMEwgFQq\nLRjizl0v/P4NzM/Po76+DgcODJRViNtsyzh37l/x2msvgiBYi+/h4aP4zGe+gFgsvaONbLlwOl1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WDQjx/84Nv44Q+/y4fjWSydeOKJP8G9974PYnF1RaTNZsfa2hqamgqHzDmQJMUPcFuty3jp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i4vjnn3+YO3cu8fHxBAUF4eTkxMaNG0VhERgYWKOtaWOmoqKClJQUSkpK8PLywsHB4aEe\nn5GREVZWVlhZWRnMDt9uNZuTk0NZWRmAQUNwbVazd6KoqAiFQoGFhQVt27Zt8k5S+uVZd1pJ02q1\n/Pzzzyxbtoz+/ftz7NgxcRWpWbNmyGQynnvuOYPHfZScO3cOBwcHWrVqJZYYmZqaIpfL8fb2NujX\nad++PXK5vNpj6M7DsmXL+P7773FwcKBLly785z//oUePHmi1Wnbv3k14eDhLliwRBYTuPKxbt47y\n8nLs7Oz46aefGDNmDKGhoQbmEBISjQFJQEhINDDuJ7NCx1dffUXPnj3x9PQ0uD0sLAytVssTTzxB\nZGQk7du3r4c9frwwMjKiWbNmXLx4ka1btxIWFsbBgwextrbm2rVrYn9FTEwMly9fprKyEk9PT9Fm\nNigoCF9f30ZX9qJSqUhJSaGoqAgvLy8CAwMblDC6m9WsUqmkuLjYwGr29gyL2xOdS0pKUCgUAAQE\nBNTaLNxU0LdkbdWq1R0tWf/8808iIiLw9/dn7969d7XIBh7p66VXr17Ex8cjk8mQy+W89tprmJqa\nEh4eXs2iW61Wk52djUqlIjc3V3w9CYJAcXExb731FmfPnmX8+PFcunSJjRs38s8//7Bt2zaefvpp\nRo0axcqVK/nkk0+wt7dHpVJhbm7OypUrmTt3rlgm+3//93+MHDmS5s2bP6rTIiHxwEgCQkKigXGv\nmRX6fPXVV8ycOdPgtu3bt/Pkk08iCAJRUVEMHDiQxMTEf52EK3HLXaa0tJQTJ04YzFq6ubnh5ubG\n4MGDxdvUajXJycmisNizZw9JSUkYGxvj5+dn4Ajl7u7e4Bx89HMNPD098ff3b1DC4W7oW83qo9Fo\nxBKowsJCMjIyqKiowNTUFAsLC3H1wtfX1yBUr6mib8navn37WsuzEhISmDNnDqampsTExBASEvKQ\n9/TB0O8paNeuHbNnzyYyMpLw8HADi+758+ezevVqKioqKCsro0+fPmzbto0nn3wSIyMjYmNjuXDh\nAmvXrmXAgAGYm5uTl5dHmzZtWLhwIQsXLmTkyJH8+OOPhIeHs379eszNzcnPz+eNN95g8ODB/PHH\nHwQGBhIUFPSoToeExL9GEhASEg2ImjIrVCoV+/btw9zcnLZt2+Lq6mrwN8ePH+f69esMHz7c4Pbu\n3buLv4eHh7N161ZiY2N54YUX6vcgHgMCAgLuOcjJ1NQUf39//P39GTZsGPC/Ov7Lly8TFxfH6dOn\n2bx5MxkZGVhbW4v5FbrUbV3T6sOkqqqK9PR08vLyGlSuQV1hYmJC8+bNDWZ/KyoqUCgU3Lx5U0wD\nTktL4+rVqwbBa3fLR2hMKJVKsUwyMDCw1lWWzMxMFixYQFpaGgsWLKBHjx6NSkjejq4JHCAkJISk\npCTs7e2xs7Nj7ty5bNy4kaeeeopDhw7xySefsG7dOvz8/Dh16hQAQ4YMAW6ZCERHRyMIAlqtFpVK\nRVBQEJMmTWL69Ol4eXmhVCr59ttvWbFiBUOHDmXo0KGP7LglJOoKSUBISDQg/vjjD1JTU/Hw8AAQ\nPdj37NlDVVUVZmZmWFpa0qdPH8LDw+nSpQtbt27l5Zdfvmt5hf4XpsSjRVfH36FDBzp06CDeLggC\nJSUlYtP2gQMHWL58Ofn5+djb2xsIi6CgIGxtbet8EKcfiObu7k7nzp2blHCoCZVKRVpamugyFBwc\nXO286gevZWdnV8tH0O+xaAwN9DqxVF5ejq+vb60rkwUFBaxcuZL//ve/zJw5k5deeqlBvR7UajVq\ntRqNRoNGoxFXkW631D1w4ABPPvkkTk5OJCYmMn/+fDHp3tPTEwcHB7Kzs9myZYu4anjw4EFeeOEF\nXnzxReRyOX5+fmRkZODn50d+fj7r169n4cKFyGQyvvnmG1599VXx3IwdO5a0tDS++uortFots2fP\nloSDRJNCcmGSkGhA1JRZcfr0aZKSkoiIiGDo0KHExsYSGRlJVVUVmzdvpnv37uzatYu+ffuKf5ee\nnk5GRgZPPfUUWq2W1atXs2zZMhITE8WZVYnGg846ND4+nosXLyKXy0lISECpVOLq6mqQX+Hv71+t\nlv9e0Gg0ZGZmcu3aNVxdXXF1dW0UA+F/g86eNDc3V3QZup/zpm81q59jodFosLKyMuivaNasWYMY\neOtK0goKW2CNVQAAIABJREFUCvD29q61Cb68vJx169axY8cOJk+ezOjRoxtkzsXcuXOJiIgwuG3O\nnDmMGTOG4OBgEhIS8PDw4OOPP2bbtm0olUqcnJx48803mTVrlriKtG/fPsLCwigtLcXX15fo6Gj6\n9u1LZWUlrVu3ZurUqcybN49vvvmGN998k5YtW2JlZcWMGTMICwujRYsW3Lhxg7179zJ8+HCxtPHq\n1av4+fk99PMiIfEvkILkJCQaO3PnzuXgwYPI5XLWrFnDu+++i1Kp5I8//qBv376MHz+eX3/9lZSU\nFIyNjdFqtQiCQEJCAmFhYSgUCiwtLenQoQPz589HJpPh6OhY59kVvXv35tSpU+IAw9XVlcuXL1fb\nThAEpk2bJjaHjxs3jiVLlogDmPPnzzN27FguXbpEUFAQX375pcEMvYQhWq2WtLQ04uPjxWC8y5cv\no9Fo8Pb2NrCa9fLyqnEAWF5eTlZWFnl5eTg7O+Pu7t7khYNGoyErK4usrCzc3NxwdXWt08H9vVrN\n6hyhHoaw0LdklclktYoltVrNN998w5o1axgxYgRTpkxp8q5TcOu9tGrVKiIiIvjuu+/EPqZTp04x\ncOBA1q5dyxtvvEFVVRXdunXjxo0b7N69m9DQULRaLcXFxWzZsoVdu3axefNmKcdBojEjCQgJiabA\n/Pnz+eyzz8jMzDRIUW3evDkff/wxM2bMuKcB399//82CBQuYN28eoaGhdbqPvXv35s0332TcuHF3\n3C4mJoaVK1dy5MgRjIyM6N+/P5MnTxZTWP38/JgyZQqTJk0iJiaGFStWSEnfD0BVVRVXr17l4sWL\nxMfHI5fLSUlJwczMjICAAIKDg/H39+f8+fPs2LGDuXPn8uKLLzbIGea6RKvVkp2dTXp6Om3atMHD\nw+OhiiVBECgrKxNFhVKppKysTHSQ0hcWlpaWdVKeJggC165dIz09/Y4CUavV8uuvv7JkyRJ69erF\n9OnTH4vmcX0SEhJ477330Gg0HDt2jIMHDzJhwgTs7Oz45ZdfRNF16NAhXnvtNQYMGMCgQYMwMzNj\n+/btnDt3jvDwcN57770m/16SaNJIAkJCorFTWlrK+++/z9mzZ7lw4YJ4e3JyMr6+vqxdu5YJEyYA\nkJiYyNatW6msrKRbt2707duXVq1aiX+zf/9+hgwZQm5uLg4ODqjV6jr7krtXAdGtWzdGjRrFO++8\nA8CXX37Jhg0bOHXqFIcOHWL06NFkZmaKAycPDw/Wr1/PoEGD6mQ/H2d0g9f4+Hi+/PJL9uzZg0wm\nQ61WY2VlZbBaERISgp2dXaNuktVHPz3awcEBmUzWoBqg9a1mdT8VFRWi1ax+f8W9lqcJgkBeXh4p\nKSm0atUKT0/PWi1ZT58+zdy5c/Hw8GD+/PnV7KAfJ2JiYggPD6dFixZcv36d8ePHs2rVKoPwOCMj\nI/bu3cvatWvJysqioqKC4OBgVq5cKa08SDQFpCA5CYnGzo0bN8jJyaGqqoqUlBTs7e1RKBR8+OGH\nODg4iOFEX375JVOnTqVdu3ZYW1vz008/0atXL7Zu3YpGo2Hv3r2sWrUKCwsLqqqqAGoVD1qtFiMj\nI4NBSlJSEuPGjWPVqlU88cQTNf5deHg406ZNIyAggIULF9K7d+9q28jlcoMsCv2wJrlcTmhoqMHz\nhoaGIpfLJQFRBxgZGXHw4EEWLVpEnz59iI+Px9HREUEQKCoqEm1md+/ezcKFCyksLMTJyUm0mdUJ\nDGtr60YjLARB4MaNGyQnJ9O8eXOeeOIJLCwsHvVuVeNerGYLCgoMrGb1+ytsbGwMVul0lqzW1tZ3\ntGRNTExk3rx5qFQqoqKiaN++faO5tvVFv379OHr0KD///DP//PMPwcHBgGECNcCLL77IoEGDqKys\npLi4uJo7noREU0cSEBISDZi8vDyuXbtGRkYGHTp0oKSkBLhlO7hy5Urc3d3ZvXs3c+bMYcSIESxY\nsAArKyv27t3L5MmTWblyJR999BEqlYqLFy8Ct7IKzMzM+OCDD4iMjCQvL4+cnBycnZ2xt7cX67F1\nM21wK+n62LFjtQ4uli5dipubG/n5+Zw/f54XXniB8+fP4+PjY7Dd7YFNLVq0QKlUIghCtft09+uO\nWeLfU1ZWxr59+3B2dhZvMzIyws7Ojl69etGrVy/xdq1Wy/Xr10Vh8eWXX5KYmEhFRQUeHh5iMF5w\ncLAYjNeQBp+6QbSlpSXt2rUzKP9rLNRkNQu3StR0wkK3yqBSqTA2NqaqqgpTU1NkMhkODg41rjpk\nZ2ezaNEiLl26JIr9+rp20dHRbNmyhbi4OEaMGMGWLVtq3G7r1q18/vnnXL16lebNm/PGG2+waNEi\ncaLjXvus/i0+Pj688MILHDlyhKNHjxIcHGwgHvTPk7m5Oebm5gZZMBISjwuSgJCQaMDk5OSQmZnJ\npk2bePHFF0lNTUWtVmNjYyNavf700084OzuzfPlycQZzxIgR/PLLLxw9epSPPvqInj17EhwcjJOT\nE9u3b+f48eNiuurOnTvZuHEjKSkpmJiYMHz4cKZPn24QZJeUlESrVq3E57ydLl26sHPnTl555RUS\nExPp3r07+/fv54MPPjDYTj+wCW6lbtvY2GBkZFTtPt390pdz3fHmm2/e87bGxsa4uLjg4uLCwIED\nxds1Gg3Jycli4/bPP/8s5gj4+voaBOM97B4DuJUerQvqu1OuQWPGzMyMli1bitarOkvW0tJSPDw8\nMDIyoqioiMzMTI4fP86+ffsICAjA19cXhULB2bNnmTlzJhs2bKj3Bm4XFxdmzpzJwYMHKS8vr3W7\nsrIyPvvsM7p06UJeXh4vvvgiy5cvZ9q0aeI20dHRdy2TrAueffZZBg4cSGRkJO+88w7m5uYGEyoS\nEhKSgJCQaNBkZmaiUqno1KkTFhYWBAQEiPfpSo2uXLnCP//8IyabdujQgeHDh5OVlYWtrS0ajQal\nUklqairDhg2jWbNm9OvXDxMTE0pKSmjbti2RkZHY29tz8eJFVq5cSUREBJ9//jlWVlZoNBouXryI\nu7t7tRUCfXSNqW3atKk1cyIkJIQLFy7QuXNnAC5cuCAm2YaEhLBixQqDL+qLFy/y3nvv1eUplfiX\nmJiY4Ofnh5+fn+hrr7Mz1QXjnT17lm3btpGeno6lpSWBgYEGwXhOTk51PhgrLS1FoVCgVqvx8fG5\n42u1qaArbSwsLKzVkrVjx44MGTKEDRs2sH//frGvYvHixWzfvp22bdvStm1bnnjiCQIDA+t8H19+\n+WXglolDZmZmrdu9++674u+urq6EhYXx+++/1/n+3AvOzs688sorHD9+nIiICBYuXIhWq23y7mQS\nEveDJCAkJBoogiCQmZlJ8+bN8fLyqna/bubwypUrvPvuuwQHBxMXF8eff/7Jli1buHnzJiNGjKCq\nqoqcnBxyc3Pp1KmT+LeCIGBra8vTTz9NdnY2dnZ2hIaG0rJlS2bMmMGZM2fo1asX5eXlJCQkEBwc\nXOMXaFFREX/++Sdnz55FJpOxd+9ejh07xmeffVZt2zfffJOVK1fy3HPPYWRkxIoVK8RVit69e2Ni\nYsLnn3/OxIkT2bBhA4BBvoVEw8TIyAgLCwtCQ0MNHL50pWlyuZy4uDh+++03Vq1aRV5eHnZ2dtUS\nt5s3b37fwqKiooLk5GRKS0vx8fExMA5oquiH/clkMvz8/Go8bxqNhu+//56oqCiGDRvGb7/9Jq7I\naLVaMjIyiI+PJz4+nuvXr9eLgHhQjh07Jk4u6LiXPqu6olevXnTr1o1NmzYxY8aMx8LKVkLifpAE\nhIREA+XmzZv8/fffolDQarXVyg1UKhWhoaEYGxsbzNSXlZWRnZ2NkZERlpaWpKamIggC7dq1E7cx\nMjLi5MmTLF26lLi4OLKysrC2tsbNzY34+Hixdrq4uBiFQiH6ot9OVVUVs2bN4vz585iamhIdHc1P\nP/1EQEAAR48eZciQISQnJ9OmTRveffddUlNTxf0YN24cEyZMQBAEzMzM2LlzJxMmTGDatGkEBQWx\ne/fuerNwvdea6sjISLZu3UpaWhoODg5MmjSJTz75RLzf09OTnJwcUVx169aNQ4cO1cs+NzaMjIxE\nkfr000+LtwuCQH5+vhiM99133xEfH09JSQkuLi4GwXgBAQE1WppmZ2dTUFCAUqnEy8uLoKCgJl9i\notVqxZ4oFxcXOnfuXKOoFwSBw4cPs2DBAp5++mkOHz4slizqMDY2RiaTIZPJeP755x/WIdwTmzZt\n4u+//xbzYuBWn1VwcDDm5ubs2LGj1j6ruqJVq1bMnj2bdevWSeJBQqIGJAEhIdFAsbGxYerUqeTl\n5QHUWBJkbm7OmDFjGDt2LJ07d+b555/H1tYWtVqNm5sbFhYWqNVqFAoFzZs3x97eXiwRUqvVTJ48\nmeLiYhYvXkybNm3Iy8vj22+/JT4+Hl9fX+CWE1Rubm6t2RGOjo7s3buXJ554gtmzZ/P+++8DEB8f\nz/bt2/H09MTHxwcbGxtmz57NkiVLWLZsGXBLfOgP+jp16sQ///xTp+fxTtxLTbUgCHz11VeEhoai\nUCgYMGAA7u7uvP766+I2+/bto1+/fvW9u00GIyMjHB0d6dOnD3369BFv158Vj4uL44svvuDy5ctU\nVVXh5eVFcHAwnp6enDhxguPHjxMTE8PTTz/d5IWDzpI1OTkZBwcHOnXqVKsl69mzZ5kzZw5OTk58\n++234vu4sbB7927Cw8M5fPiwQQ5Fly5dxN/ffvttvv322xr7rOqSoKCgentsCYnGjiQgJCQaKKam\npuKgVBCEWutv33jjDa5du8bs2bP5/PPPCQkJoaqqCgcHB5YsWYKZmRk3btwQnVw0Gg2mpqYkJiYS\nHx/Pd999x4svvgjcWtE4d+4cNjY2ODo6Ard6GzQazR1n+nJycigoKBAtD9VqNaNGjeLmzZt89NFH\ndO7cmV9++YVZs2bh7+9P//79AZgyZQopKSnMmjULuVyOWq2mT58+Br0et6PVagHEPgud5WxBQQGX\nLl3Cy8sLFxeX+znVd+Q///mP+HtAQAAvvfQSJ06cMBAQEnVDbbPiarWa+Ph4Vq5cycaNG2nbti3N\nmzfn008/xd/fX2zcbtu2LS4uLg8l2flhUVhYSFJSEs2aNaNDhw61WrImJSUxb948iouLWbZsGR07\ndmx0wurXX39l/Pjx/PLLLwarpTVRW5+VhITEw6HpfMpKSDRBNBoNwB0HAqampkydOpVt27YxdOhQ\ntFottra2DBkyBCsrK0xNTUUrwmXLlnH+/Hng1qDf3NycjIwM8bHS09NZu3atOPOm0WhITEzE3t6e\nNm3a1LoPKSkpGBsbi7OdX3/9NWfPnuXMmTNMnDiRJ598kvDwcNq1a8fXX38tZlHk5OQQHx/P1KlT\nOXz4MFFRUfTr149Tp06Jj60TDDdv3gRuDTKNjY0xMjIS/wU4ffo0n3zyCWfPngVuiSG4VUv98ccf\nExcXV22/w8PDcXBwoHv37vzxxx+1Hp8OQRCIjY2tVpsdFhaGo6MjAwYMMAj8k6gbtm7dyttvv03b\ntm1JSUnhyJEj/PPPP+K1dXd3588//+TDDz+kW7du9OvXjw8++IC1a9dy7Ngx8vPzG91gU6lUcu7c\nOdLT00WBVJN4yMnJ4f/9v//HO++8w/jx4zl48CCdOnVqMOJBrVZTUVGBRqNBo9FQUVGBWq2utt3R\no0cJCwtj586dosmCjqKiIg4ePCj+7fbt2zl27JiUDyMh8SgRBOF+fiQkJBohWVlZwrRp0wQXFxeh\nVatWwoEDBwSVSiUMHjxYCAkJEbZt2yYsWrRI6N69u2BkZCRMnjxZEARBKC8vF0aNGiV07dpV0Gq1\nNT62VqsVIiIiBFdXV0GlUglFRUXCmDFjBEtLS2HNmjXCb7/9JhQVFQmCIAhRUVGCp6enIAiCUFpa\nKjz11FOCvb29cOTIEaGwsFC4evWqEBQUJPTr10/QaDTicxw4cEDo2bOnYGNjIwQHBwsnT54Ujhw5\nIhw/flxQq9WCIAjC9u3bBTs7OyE9Pd1g/z777DOhRYsWwqFDhwRBEMTtT506JRQXFwsVFRXCli1b\nBBsbGyEpKemO53H27NlCaGioUFFRId52/PhxoaysTCgtLRUWLVokODk5CYWFhfd8bSTuzo4dO8TX\n0N3QarVCYWGhcOzYMeGLL74QJk2aJDzzzDNC27Zthb59+wrvvfeesGbNGuH3338XcnJyBKVSKZSW\nljaYn/z8fOGvv/4Sjh07Jly7dq3W7a5fvy5Mnz5dCA0NFb7++mvxdd3QmDNnjgAY/MyZM0dIS0sT\nmjVrJqSlpQmCIAi9e/cWTExMhGbNmok/gwYNEgRBEHJzc4VOnToJNjY2QosWLYQuXbqI72cJCYk6\n5540gZFwf7MyjWsKR0LiMUKr1YqzrPoz8zWhUqkwNzfn9OnTLFu2jBMnTtClSxeeeeYZPvroIxYs\nWMD06dMpKipi8ODBhIaGEhMTU+NjVVVVMXLkSK5fv84ff/xBdnY2o0aN4uLFi3h4eHDp0iWUSiXN\nmzenuLgYT09PkpOTSUhI4OWXX6ZXr16sX79efLw5c+bwxRdfiL0fZ8+epVu3bjz11FNMnjyZ5ORk\nfvnlFwoLCwGIi4tj6dKlLFu2jIqKCj788EN69+5Np06daNWqFeHh4ezdu5ejR4/i5ORU6zkZNGgQ\nzz//fK011dHR0axYsYLY2FiDjIzbCQwMJDIykhdeeKHWbSQePoIgkJOTQ1xcHBcvXiQ+Pp5Lly5R\nVlaGu7u7QX6Fn58f5ubmD3UWX6VSkZqaSmFhIT4+Ptjb29f4/CqVik2bNrFp0ybGjx/PxIkTG2S6\ntoSERKPlnj74pB4ICYkmwp3qvgVBEHMjjI2NRWejzp078+OPPwK3ypWqqqpwdnYW7V7LysqQy+Wi\n339NlJWVceXKFTHF2NnZmeLiYoYNG0Z0dLSYdH3lyhUuXLgghtElJyej1WrF59JqtWi1Wm7evCkO\n9AsKCoiKisLX15f//ve/GBsbU1lZSUlJCcuXLycsLAyA/v3788MPP5CWlsbRo0fZuHEjr7/+Op9/\n/jnp6em0bNkSW1tbLly4gLe39/9v796DoirfOIB/10XkslwWMAQRSBRdVgUd8H4BaxRHjFGcinBQ\nxDFtMp2mzLyjoBgNmjhjjcoIIw42XrAmEUssyCaMioVWa1FaVxEQNogFQVzg94ezR1ZA134uCn4/\nf+2e8+7hvF6G8+zzvs/TZXO6R62pTk1NRWJiIvLy8h4ZPDzuOvTsiEQioU+JYQ8OcP/fvVqtFjZu\n5+TkoLS0FG1tbfDx8RFKzBo2cD/tXgAdS7J6e3t3W5K1ra0NJ0+eRHJyMsLCwnDx4sUXotcFET2f\nGEAQvQBEIlGXDz6GrEW/fv0gFoshFouNNge7u7ujvLz8kdeuqalBUVER3n77beHYpEmTkJubi8LC\nQgQGBmLgwIEYNWqU0FQKuL/p08LCQtic3a9fP7S0tODPP/8UjpWWlqKoqAjh4eHC+QEDBmD69OnY\nsWOHME4qlcLR0RFeXl44ceIEqqqqIJFIUF1djerqatTV1WHTpk04ffo0KioqMH/+fLz55pt49dVX\nYWFhgWPHjiEvLw+fffZZp/llZGRg/fr1uHDhAoYOHWp0TqPR4MaNGwgKCkJbWxtSUlJQU1ODKVOm\nPO6vhJ4TYrEYPj4+8PHxQXh4OID7Afe9e/egUqlQXFwMhUKBo0ePQq1WY8CAAULTRrlcDrlcDldX\n1yfeuP1wSdYJEyZ0eY329nb88MMP2LZtGwICApCdnQ03N7enMnciov+KAQTRC6yrB5bW1lajYKOr\nb+s7srGxQWRkpFGd/w8//BB//PEHPv74Y4SHh8PX1xd37txBWVkZYmJiIJVKUVpaColEAi8vL+Fz\nDQ0NUKlUWLRoEQDg9u3bqK+vx7hx44x+ZlVVFTw9PYVsRmVlJSorK4UHdycnJ/Tv3x9Xr15FWVkZ\n2tvb4eHhgQsXLuDUqVNYv3498vLyUFtbC7FYjJEjRyIrKwu+vr7Iz8/HnDlz0NDQAADYuHEjtFot\ngoKChJ+/aNEifP7559DpdFi5ciWuXbsGKysr4QHP2dnZpD///8LU/hVbt25FQkKC0fKW4uJiIQgq\nKipCbGwsrly5AplMhkOHDiEgIMBs992biEQiWFpaCl2aDdrb29HY2IjLly+jpKQEFy5cQEpKCqqq\nquDg4AA/Pz8hsPDz84Ojo2OnbEJbWxv++usv/Pvvv48tyVpcXIwtW7bA3t4eaWlpj6xORkTUkxhA\nEJGRJ12i4ebmhoyMjE7H9uzZg+TkZCQmJqKlpQUvv/wyZsyYIQQkpaWlcHFxMSq5qtVqUVlZKTzI\nSiQS3L59WwgUDFQqFfr37y9Ufbp16xZqa2uF0o+Gyk2GNeWbN2/G6tWrAdyvIX/06FH4+voiPT1d\nKAVrMG3aNCF4AO5XmOqOXC5HcXHxE/15PQ2m9K8AgDfeeANHjhzpdLylpQXh4eFYs2YN3nnnHXzx\nxRcIDw9HaWmp2Rr39QUikQgSiQTjx483qhTU3t6Of/75ByUlJSgpKcGJEycQFxeH+vp6uLm5QSaT\nQSaToampCampqXjllVewefPmbvcuqNVqbN++HZWVldi5cycmTJjw3FRVIiICGEAQ0f/JsL/i4cDD\nz89P6CTb2tqKiooK2NjYwMLCAo2NjRCLxXBxcTHq8qpWq9Hc3Cx80zpmzBi0trbi0qVLCAwMFB5u\nT58+DUtLS+Hb9Fu3bkGv1wt9KAz38vfff8Pe3l7Ijuj1ejg4OECv10MikQB4dIncvur777+HXq/H\nmjVrIBKJ8N577+HTTz9Fbm4uS2P+ByKRCM7OzggODkZwcLBw3LBM6auvvsKePXvQ2toKV1dX5Obm\noqysDDKZDKNGjYJMJsOwYcNQV1eHpKQkFBQUYOvWrZgzZ45Ze1rs27cPhw8fRklJCSIjI3H48OFu\nx+7evRu7du3CnTt3sHDhQuzfv18IgNRqNWJiYlBQUABPT0/s27ePjRWJ+jj2gSCi/8uj9le0trYK\nwYWHhwecnJwAALa2tsjOzkZqaqrRZxobG+Hp6Sn0nJBKpYiKikJcXBwyMjJw7tw5hIWFQaVSYciQ\nIXjppZcA3F/CZGlpKQQQhuU9169fh42NjZCpEIvFaGlpgUajgbe3t5Cp6G1M7V/x9ddfw8nJCXK5\nHPv37xeOK5VKjBkzxih4GjNmDJRKpTlv+4VTXl6ODRs24OTJk0hLS4NKpUJ+fj5+/fVXfPLJJwgK\nCsLVq1eRmJiIqVOnIjAwEIGBgfj5558xd+5cszfEc3d3x8aNG7F06dJHjsvJyUFiYiLOnz+P69ev\no6ysDFu2bBHOR0ZGYuzYsdBqtUhISMDChQuFKmpE1DcxA0FEZvG4h5/29nbhQd9gwYIFRhut+/Xr\nh+TkZDQ0NGDZsmUYN24c/P39MXr0aGHvRHNzM9ra2qDT6dDY2AhbW1sAwN27d1FeXg4nJydhT4JI\nJEJFRQW0Wi2GDx/eKzsW79q1C35+frC0tERmZibmzZuHoqKiTp3CX3/9dSxfvhyurq4oKChAREQE\nHB0dERkZiYaGhk4VfBwcHKDT6XpyKn2eVqtFZGQkZs+ebRSsWVhYYMSIERgxYgQiIiIAPNi43ZNL\nyAz/1woLC3Hz5s1ux6WlpSE2NlZooLhp0yZERUUhMTERKpUKv/32G86dOwdra2tERERgz549OHHi\nBFasWNEj8yCintf7fnsSUZ/QXanKjm7evImGhgYcO3YMzc3N+OmnnxAcHIza2lphDbqVlRVmzZoF\ne3t7vPbaa9iyZQtu3LiBmpoaVFdXCw/WhmurVCqIxeJO+yp6iwkTJsDOzg4DBgzA4sWLMWXKFJw5\nc6bTOD8/P7i7u0MsFmPy5MlYvXq1ULJXIpGgvr7eaHx9ff1jN8zTkwkICEBoaKhJy+QMG7efR0ql\nEv7+/sJ7f39/VFVVQavVQqlUdiqN7O/vz2wWUR/HAIKInhuGjIChj4JSqcS6deuwc+dOnD9/HsnJ\nyVi+fDnkcjnCwsKEsVOnTkV6ejpkMhkKCwuFzdNXrlzBwIEDATwIIBQKBQYPHiwsf+rtTO070XGc\nYfN3x88VFxcL3zATdfRwxsrwWqfTMZtF9ILiEiYieu4YvrGVyWQYNGgQDh48iO3bt0MqlSIyMhIf\nfPCB0GxOJBKhf//+nTawtre3Iz8/H1ZWVgAeBCeXLl2Cra2tsNSpN6mrq0NBQQFmzJjx2P4Vp0+f\nxvTp0+Ho6IhffvkFe/fuxY4dOwDcLwUrFouxd+9erFixAgcOHAAAzJw5s0fnQ73Dwxkrw2s7Oztm\ns4heUMxAENFzy9PTE0lJSbh27RoaGxuFB2HDpuiO2traoNfrhUyDSCTC8OHDMWTIEAAPAojDhw/j\nm2++gaOjY89N5Cm5d+8eNm7ciIEDB8LFxQUpKSlG/SsMlaUAIDMzE8OGDYOdnR2io6Px0UcfYfHi\nxQAAS0tLZGVlIT09HY6OjkhNTUVWVpbZltAEBwfDysoKEokEEomk234Gc+bMEcZIJBJYWloKpXkB\nwNvbG9bW1sL5WbNmmeV+yZhcLodCoRDeKxQKuLq6wtnZGXK5HGVlZUYZB4VCwWwWUR8nMiX13cET\nDSYiepYe7vFAz0ZwcDAWLVpkUu+Khz83c+ZMbN68GcD9AOLgwYMsEfqU6PV66PV6xMXF4ebNmzhw\n4AAsLCw6FTc4e/YslixZgtzcXLi7u2PBggUYP348EhMTAQATJ07E1KlTER8fj+zsbMTExKC0tFRY\nPkhEvYpJvzSZgSCiPovBQ++lVquRn5+P6OjoZ30rfVZ8fDysra2RmJiII0eOwNraGvHx8dBoNJBI\nJNBoNACA0NBQrF27FiEhIfD09ISXlxfi4uKE62RmZqKwsBBSqRTr1q3D8ePHGTwQ9XHMQBARkVkF\nBweMQlfXAAAENklEQVRDqVSivb0dI0aMQEJCgtF+la5s27YNubm5Rn0uvL290dTUhLa2NowdOxZJ\nSUlG1YGIiOj/xgwEERE9e7t27UJZWRnKy8uxfPlyzJs3D9euXXvkZ9LT07FkyRKjYxkZGVCr1bh+\n/TpCQkIwe/Zs1NXVmfHOiYioK8xAEBFRjwoNDcXcuXOxatWqLs//+OOPCA0NRWVlpdHG8IeNHDkS\nSUlJmDdvnrlulYjoRcMMBBERPX8e17siLS0NCxYseGTwYMp1iIjIPBhAEBGR2dTV1SEnJwfNzc3Q\n6/XIyMhAXl4eQkNDuxzf1NSEL7/8stPyJY1Gg4sXL6KlpQXNzc1ISkpCTU0NpkyZ0gOzICKijhhA\nEBGR2TxJ7woAyMrKgqOjI0JCQoyO63Q6rFy5ElKpFIMHD8bZs2eRnZ0NZ2dns88hMzMTMpkMtra2\n8PHxQX5+fpfjdu/ejUGDBsHe3h5Lly7F3bt3hXNqtRohISGwsbHByJEj8d1335n9vomIzIV7IIiI\niLrx7bffYtmyZTh27BjGjx+PiooKAMDgwYONxuXk5CA6OlrolTB//nxMnDhR6JUwadIkTJo0CQkJ\nCThz5gxiY2PZK4GInkcm7YFgAEFERNSNyZMnIzY2FrGxsY8c99Zbb8Hb2xs7duwAAJw/fx5RUVGo\nrKyESqXC6NGjUVNTAzs7OwDAtGnTEBUVhRUrVph9DkRET4CbqImIiP6r1tZWFBYWorq6GsOGDYOH\nhwfeffddNDU1dRqrVCqNelL4+/ujqqoKWq0WSqUSQ4cOFYIHw3mlUtkj8yAietoYQBAREXWhqqoK\n9+7dw/Hjx5Gfn4+ioiL8/vvviI+P7zS2oaEBDg4OwnvDa51O1+mc4bxOpzPvBIiIzIQBBBERURes\nra0BAKtWrYKbmxtcXFzw/vvv48yZM53GSiQS1NfXC+8Nr+3s7DqdM5zvmJEgIupNGEAQERF1QSqV\nwsPDAyLRgyXBHV93JJfLoVAohPcKhQKurq5wdnaGXC5HWVmZUcZBoVBALpeb7+aJiMyIAQQREVE3\nYmJikJKSgtu3b6O2tha7d+9GWFhYp3HR0dE4dOgQLl++jLq6OsTHxwu9LHx9fREQEIC4uDg0Nzfj\n1KlTKC4uRkRERA/Phojo6WAAQURE1I1NmzYhKCgIvr6+kMlkGDt2LDZs2ACNRgOJRAKNRgMACA0N\nxdq1axESEgJPT094eXkhLi5OuE5mZiYKCwshlUqxbt06HD9+nCVciajXYhlXIiIiIiICWMaViIiI\niIieNgYQRERERERkMosnHG9SWoOIiIiIiPomZiCIiIiIiMhkDCCIiIiIiMhkDCCIiIiIiMhkDCCI\niIiIiMhkDCCIiIiIiMhkDCCIiIiIiMhkDCCIiIiIiMhkDCCIiIiIiMhkDCCIiIiIiMhkDCCIiIiI\niMhk/wPnb64JFgwO5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e17940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$\", fontsize=18)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZOXIkUVFRjBw5kqVLlzodUrfz1jQw768fcM2T6zjS0ML/zp7EH+ecG7bF1dKl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\nYImIiIiIiHQSFVgiIiIiIiKd5P8DvuCVmUKbxrwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d3a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178da198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1178ae5f8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OqeW5Ppmw5Ts8FIcfUolJwd7aCqNilb8nV+ez/V22gjg3dQ7WgxbaFrRpjyfz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c940f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.29161715507507324\n",
      "[LibSVM]1 0.01 0.2936241626739502\n",
      "[LibSVM]2 0.001 0.34351515769958496\n",
      "[LibSVM]3 0.0001 0.6209981441497803\n",
      "[LibSVM]4 1e-05 0.9892549514770508\n",
      "[LibSVM]5 1.0000000000000002e-06 0.8884930610656738\n",
      "[LibSVM]6 1.0000000000000002e-07 0.9605729579925537\n",
      "[LibSVM]7 1.0000000000000002e-08 0.9561920166015625\n",
      "[LibSVM]8 1.0000000000000003e-09 0.9639899730682373\n",
      "[LibSVM]9 1.0000000000000003e-10 0.991912841796875\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1168d5940>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FvoPxu9CKDD4KepEEMq9kJM2tzu/f0PKNxI6CXiSBTBudy4m56Typc99IDCnoRRKImTG3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119e1ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W3jx/Bl0d7dz12BbesmAGj27aydP/upcE7D84wOSuDtrbgl37+5k6qZN9hwZ4YuseAP74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\nN0NDkpSboSFJys3QkCTlZmhIknL7/5K3lTvNE+NqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117926320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56780998] [[[ 2.28129013]\n",
      "  [ 2.71597487]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[ 2.27128546  2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aT0I9cz8/P3r16sXYsWONHfI7d+7w119/6RyhYqlUzjatF01fOQ2civ9zE1AR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\nDhxgyZIl1KlThwsXLjBw4ECCgoKYPn26WzR4Mue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vxJO8xgJTgS1AvHf5dp8+fViwYAH3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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168c1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.062485     2.24179316   1.79750198]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkSNp1qwZ69aty9drDTKfBrooiUajweUtFzQajdJS8hWqQVeYIkWKMGvWLPbt20eFChU4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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fbb3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, n_iter = 50, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [ 0.28480668] [[ 1.05542422  1.09851637]]\n",
      "SVC:                          [ 0.31933577] [[ 1.1223101   1.02531081]]\n",
      "SGDClassifier(alpha=0.00200): [ 0.32] [[ 1.12293103  1.02620763]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        n_iter=100000, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZM2eSmPji61vMnj0bW1tbmjVrBkDHjh25e/cu8+bNS7F8VFRUisd37txJ165d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\n8hs2bAAgKSmpzHGLhIpGPKVcClbpR3/KYE6ePJ5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z4GvgUf4zjX6y1wTwNPAycCmwFpjt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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67f77b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "mnist = fetch_mldata(\"MNIST original\")\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"]\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82633333333333336"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, 82% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9224"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we cut the error rate in two), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default).\n",
    "\n",
    "**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\"ovr\"` if you want to use the OvR strategy instead (OvR is the default since 0.19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape=\"ovr\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94615000000000005"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n",
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n",
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   0.9s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   0.9s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.0s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.0s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   40.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f7a2470>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f7a2908>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.8523160584230869, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85599999999999998"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.8523160584230869, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99965000000000004"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97089999999999999"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94996882221722923"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97466344048457532"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n",
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.4s\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   15.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.4s\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n",
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.5s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  11.2s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  10.9s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  11.2s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=  10.6s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=   9.9s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=  10.5s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=  10.4s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=   9.9s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=  10.0s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  10.0s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  11.8s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  11.5s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=   9.9s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=  10.4s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=  10.1s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=  10.4s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=  10.7s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=   9.8s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.8s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.3s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.3s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.0s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.0s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.1s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.8s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.8s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  7.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f74ec50>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f74eb00>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.7454011884736254, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.079694548186439285, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785356"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59291683855287403"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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